Forthcoming and Online First Articles

International Journal of Electronic Security and Digital Forensics

International Journal of Electronic Security and Digital Forensics (IJESDF)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of Electronic Security and Digital Forensics (64 papers in press)

Regular Issues

  • A Novel IoT-enabled Portable, Secure Automatic Self-Lecture Attendance Systems (SLAS): Design, Development, and Comparison   Order a copy of this article
    by Ata Jahangir Moshayedi, Atanu Shuvam Roy, Hamidreza Ghorbani, Habibollah Lotfi, Xiaohong Zhang, Liao Liefa, Mehdi Gheisari 
    Abstract: This study focuses on the importance of monitoring student attendance in education and the challenges faced by educators in doing so. Existing methods for attendance tracking have drawbacks, including high costs, long processing times, and inaccuracies, while security and privacy concerns have often been overlooked. To address these issues, the authors present a novel internet of things (IoT)-based self-lecture attendance system (SLAS) that leverages smartphones and QR codes. This system effectively addresses security and privacy concerns while providing streamlined attendance tracking. It offers several advantages such as compact size, affordability, scalability, and flexible features for teachers and students. Empirical research conducted in a live lecture setting demonstrates the efficacy and precision of the SLAS system. The authors believe that their system will be valuable for educational institutions aiming to streamline attendance tracking while ensuring security and privacy.
    Keywords: portable system self-lecture attendance systems; self-lecture attendance system; SLAS; automated attendance system; Raspberry Pi-based system; QR codes; internet of things; IoT.
    DOI: 10.1504/IJESDF.2025.10057973
  • The Authenticity of Digital Evidence in Criminal Courts: A Comparative Study
    by Abdullah Alkhseilat, Tareq Al-billeh, Mohammed Albazi, Naser Al Ali 
    Abstract: Scientific progress has a significant impact on both reality and the law that applies to it. As the ICT system has positive points that are considered an added value to it, as it made it easier for people to perform their tasks and facilitate interpersonal communication for individuals, saved effort and money and reduced the time needed to accomplish part of the duties, but at the same time, it has become a means of committing offences and a fertile space for the existence of offence, to the extent that offence in our current era has become the result of intermarriage between human intelligence and artificial intelligence, Thus, the issue of proving cybercrimes requires a deep exploration in the notion of the authenticity of audio evidence obtained from electronic searches, as well as the process of eavesdropping and recording phone calls, and the use of expert and inspection procedures in criminal lawsuits and its impact on proof before the criminal courts.
    Keywords: criminal courts; digital evidence; cybercrime; communication; criminal lawsuits; artificial intelligence.
    DOI: 10.1504/IJESDF.2025.10058441
    by Oday Al-Hilat, Nayel AlOmran 
    Abstract: The use of electronic means of a public official in carrying out their duties may lead to an instance wherein the person discloses confidential information, which can significantly impact their obligations. After verifying this act as part of electronic misconduct, disciplinary action is enforced upon the concerned party to rectify and ensure proper functioning in delivering public services without any disturbance or infringement. The study presents several significant findings regarding the absence of comparative regulations concerning electronic violations and their judicial evidence. It provides recommendations such as modifying legislative frameworks to enhance public utility disciplinary systems and incorporating rules for electric violations. The fundamental focus revolves around assessing, verifying, and punishing digital misconduct by management or regulatory bodies. Additionally, this research employs descriptive-analytical methods comparing the Jordanian Law with its Egyptian counterpart in exploring these issues.
    Keywords: public; official; electronic; disciplinary; violation; disclosure of secrets and proof.

  • The Legal Authority of the Electronic Authentication Certificate and its Role in Proving E-Commerce Transactions
    by Lana Al-Khalaileh, Tareq Al-billeh, Ali Al-Hammouri 
    Abstract: This article analyses the concept of electronic authentication certificate and shows its types issued by electronic authentication authorities according to the function they perform and the purpose of their issuance. It will also show the legal recognition of electronic certificates by referring to the national legislations in Jordan, Egypt, France and Tunisia, and the extent to which these legislations comply with international requirements. In this study, light will also be shed on achieving trust and safety among dealers through modern means of communication, especially via the Internet, and on encouraging dealing with electronically signed documents through the use of a reliable, neutral third party. This party will be responsible for verifying the integrity of the electronic certificates and the validity of their issuance, as well as ensuring the seriousness of the dealing and that it is free from fraud.
    Keywords: electronic documentation; legal authority; electronic documents; international requirements; electronic commerce; electronic transactions.

  • Feature-driven Anomalous Behaviour Detection and Incident Classification Model for ICS in Water Treatment Plants
    by Gabriela Ahmadi-Assalemi, Haider Al-Khateeb, Tanaka Laura Makonese, Vladlena Benson, Samiya Khan, Usman Butt 
    Abstract: Industry 5.0 envisions humans working alongside emerging technologies and enabled by the fusion of devices and sensors using information and communication technologies (ICT) to facilitate process automation, monitoring and distributed control in industrial control systems (ICS). However, the application of disruptor technologies and exposure of insecure devices broadens the attack surface making ICS an attractive target for sophisticated threat actors. Furthermore, ICS deliver a range of critical services hence disruption of industrial operations and services could have serious consequences. This study proposes an anomaly-based intrusion detection system for a water treatment plant based on a new model to determine variable significance for improved detection accuracy using machine learning (ML) algorithms coupled with incident classification based on functional impact. Determining statistical significance for independent ICS variables was addressed using logistic regression. Overall, 39 variables are deemed relevant in diagnosing the system state of the ICS operation to be expected or under attack. Our approach is validated using the secure water treatment (SWaT) testbed. Experimental results reveal that anomaly detection was effective using k-NN, ANN and SVM achieving an F1-score of 0.99, 0.98 and 0.97 respectively.
    Keywords: critical national infrastructure; fifth industrial revolution; operational technology; smart city; advanced persistent threats; APT; artificial intelligence.
    DOI: 10.1504/IJESDF.2025.10058572
  • Android Malware Analysis using Multiple Machine Learning Algorithms
    by Rahul Sahani, Madhusudan Anand, ARHIT BOSE TAGORE, SHREYASH MEHROTRA, Ruksana Tabassum, S.P. Raja 
    Abstract: Currently, Android is a booming technology and has occupied the major parts of the market share. However, as Android is an open-source operating system there are possibilities of attacks on the users, there are various types of attacks but one of the most common attacks found was malware. Malware with machine learning (ML) techniques has proven as an impressive result and a useful method for Malware detection. Here in this paper, we have focused on the analysis of malware attacks by collecting the dataset for the various types of malware and we trained the model with multiple ML and deep learning (DL) algorithms. We have gathered all the previous knowledge related to malware with its limitations. The machine learning algorithms were having various accuracy levels and the maximum accuracy observed is 99.68%. It also shows which type of algorithm is preferred depending on the dataset. The knowledge from this paper may also guide and act as a reference for future research related to malware detection. We intend to make use of Static Android Activity to analyse malware to mitigate security risks.
    Keywords: Android malware; detection; machine learning; static Android activity.
    DOI: 10.1504/IJESDF.2025.10058706
  • Exploring Advanced Steganography Techniques for Secure Digital Image Communication: A Comparative Analysis and Performance Evaluation
    by Rohit Deval, Nachiket Gupte, Johann Pinto, Adwaita Raj Modak, Akshat Verma, Anirudh Sharma, S.P. Raja 
    Abstract: This is a digital age. In a world where everything seems to be public, privacy and confidentiality have never been more important. So, the combination of this aspect of our life and this need of our age is the ability to securely hide data in the digital world in a way where it is not so easy to detect. Thus, the culmination of this thought process helped the authors arrive at the topic of our paper which is steganography in digital images. Image steganography is defined as the process of “concealing a message or piece of data inside an image file”. Image steganography is crucial in the digital era, when the transmission and storage of digital information are widespread, for protecting the confidentiality and integrity of sensitive data. To this end, it has been reviewed in the latest technology and has attempted to put forth the best techniques/algorithms by which data of many kinds can be hidden in digital images. After extensive research, it narrowed down to six techniques which would be presented in this paper.
    Keywords: steganography; digital images; data hiding; encryption; secret message; least significant bit; LSB; steganography; image processing; image compression.
    DOI: 10.1504/IJESDF.2025.10058707
  • Implementation of a novel technique for ordering of features algorithm in Detection of Ransomware Attack
    by Laxmi Bhagwat, Balaji M. Patil 
    Abstract: In today’s world, malware has become a part and threat to our computer systems. All the electronic devices are very susceptible/vulnerable to various threats like different types of malware. There is one subset of malware called ransomware, which is majorly used to have large financial gains. The attacker asks for a ransom amount to regain access to the system/data. When dynamic technique using machine learning is used, it is very important to select the correct set of features for the detection of a ransomware attack. In this paper, we present two novel algorithms for the detection of ransomware attacks. The first algorithm is used to assign the time stamp to the features (API calls) for the ordering and second is used for the ordering and ranking of the features for the early detection of a ransomware attack.
    Keywords: ransomware; machine learning; dynamic detection technique; feature selection and ordering; API calls; Malware.
    DOI: 10.1504/IJESDF.2025.10058767
  • Honeybrid method for the Network Security in Software Defined Network System
    by Sulakshana B. Mane, Kiran Shrimant Kakade, Arun Ukarande, Bhushan Saoji, Kiran K. .Joshi 
    Abstract: The social network realistically is a Using a single pause solution, ubiquity access to all of our digital requirements although familiar people are increasingly relying on large amounts of data. SDN carefully opens continuous flow controller’s performance acts as one of the key aspects towards the remarkable accomplishment of the SDN objective. End users of computer network are vulnerable to growing the number of threats posed by sophisticated online attacks. Honey pot provides a platform by which attacks can be investigated. To address the potential downside, we humbly presented a hybrid honey pot architecture that blends low and high honey pots. The low-interaction honey pot can efficiently identify and stop economic actions like port scanning. There is a lot of traffic that a honey pot with limited engagement cannot handle. A containment environment (VM ware) is commonly used.
    Keywords: security; software defined networking; honey pot; network security; intrusion detection system; IDS.
    DOI: 10.1504/IJESDF.2025.10059133
  • Secure System to Secure Crime data using Hybrid RSA-AES and Hybrid Blowfish-Triple DES
    by Raghav Modi, Ananjay Singh Jammoria, Akshat Pattiwar, Arnav Agrawal, S.P. Raja 
    Abstract: Data security is the project’s primary goal. We suggest hybrid cryptography as a technique to keep the data secure. When the sender tries to email the recipient the criminal data, it will be encrypted with a symmetric key utilising symmetric encryption. In most cases, the recipient receives a symmetric key that he can use to decrypt the data. However, we employ hybrid cryptography to further increase security by encrypting the symmetric key using asymmetric encryption and transmitting both the encrypted symmetric key and the encrypted crime data to the recipient. The encrypted symmetric key is decrypted by the recipient using their private key, and the encrypted criminal data is then safely delivered to the recipient using the decrypted symmetric key. In the Police Department data about offenders, including their background, record, and information about their prison records and officers who treated them is maintained in the cloud. Yet, that information is moved from one department to another for departmental purposes. The Hackers may hack this process and contaminate the data. We recommend hybrid cryptography as a strategy to keep this data secure.
    Keywords: Advanced Encryption Standard; AES; Rivest-Shamir-Adleman; RSA; Triple Data Encryption; 3DES; Blowfish.
    DOI: 10.1504/IJESDF.2025.10059534
  • Criminal Protection to the Digital Right to be forgotten in Jordan
    by Mohammad Alshible, Hamzeh Abu Issa 
    Abstract: Individuals’ private information may be readily accessible to third parties, which may exert harm to the individual’s reputation. The so-called Right to be forgotten was established in response to people’s urge to delete information that had previously been published about them. This issue has risen in light of the development of the digital publication. The absence of legal texts that protect this right, and occasionally the existence of legal texts that may contradict this right, such as the right to freedom of expression and the controversy about the practicality of establishing a right to be forgotten, are among the issues that may affect or prevent the enjoyment of this right and its exposure to a violation. The current study will explain the concept and nature of the right to be forgotten and the criminal protection provided by legislation and its problems in Jordanian law.
    Keywords: personal data; right to privacy; right to digital be forgotten; criminal protection of privacy; Jordanian Law.
    DOI: 10.1504/IJESDF.2025.10059697
  • Pixel recurrence based image watermarking for block based integrity verification
    by Radha Ramesh Murapaka, A. V. S. Pavan Kumar, Aditya Kumar Sahu 
    Abstract: This paper proposes a pixel recurrence-based digital image watermarking (PRDIW) scheme to identify the tampered blocks from an image. The proposed scheme obtains two mirrored image (MI) blocks consisting of 2 x2 pixels from each carrier image (CI) pixel block. Next, a one-digit integrity value (ivone) is computed from each block and encoded inside the block to identify the tampered blocks successfully. Additionally, the proposed scheme is reversible. Therefore, it can successfully recover the CI and the encoded watermark bits at the receiving end. The results of the proposed scheme suggest that the quality of the obtained watermarked images (WIs) is superior, with an average peak signal-to-noise ratio (PSNR) of 56.11 dB, 54.68 dB and 53.05 dB, 51.68 dB while watermarking 65,536 and 131,072 bits, respectively. At the same time, the structural similarity index (SSIM) for the entire obtained watermarked image is superior to that of the existing works.
    Keywords: digital image watermarking; integrity; reversibility; watermarked image.
    DOI: 10.1504/IJESDF.2025.10059766
  • Blockchain as an indispensable asset for educational institutions: A systematic review
    by Rabia Nazir, Aehsan Hussain Dar, Syed Zubair Ahmad Shah 
    Abstract: Blockchain is eminently flourishing as an open distributed data structure to record digital transactions efficiently and permanently in cryptographically linked time-ordered sets of blocks. Integration of blockchain into education opens new possibilities by providing a secure platform to share student information, conduct evaluations and track the entire learning process. In this paper, research work on blockchain technology in the education sector is studied thoroughly and systematically organised into groups to get invaluable insight into the implementation of blockchain in education. It focuses on the perquisites of implementing blockchain technology in education as it significantly opens new possibilities for the education sector by providing a secure platform to share student information, conduct evaluations and track the entire learning process. In the education sector blockchain is showing conceptual breakthroughs, however, some administrative and technological concerns are to be addressed. We analysed research articles and found that data management and certificate verification have been two main research themes. This study provides an overview of blockchain applications in the education sector, as a contribution to already existing research work. The characteristics of blockchain used in the previous research are also examined and the research gaps are summarised.
    Keywords: blockchain education; e-certification; information security; smart contracts; IPFS.
    DOI: 10.1504/IJESDF.2025.10059807
  • Methods of Teaching Electronic Administration Legislation by Using Artificial Intelligence Techniques
    by Tareq Al-billeh, Ali Al-Hammouri, Lana Al-Khalaileh 
    Abstract: This qualitative study aims to clarify the role of using artificial intelligence techniques in teaching law subjects to law students and improving the quality of educational services. The research used the descriptive analytical method by analysing the system for integrating e-learning in higher education institutions, analysing the concept of artificial intelligence in the field of e-learning and the role of artificial intelligence techniques in improving the quality of education in law subjects in general and electronic administration legislation in particular. The findings indicated that the use of artificial intelligence techniques in teaching law subjects at the Faculty of Law. It has improved the quality of education about law subjects in general and electronic administration legislation in particular. The need to Faculty of Law should equip lecture halls with the latest artificial intelligence devices and technical and technological equipment to keep pace with recent developments.
    Keywords: teaching methods; artificial intelligence; quality of education; electronic administration legislation; faculty of law; e-learning.
    DOI: 10.1504/IJESDF.2025.10059889
  • Dispute rules applicable to electronic commerce contracts
    by Mohammad Al-Freihat, Abdullah Khsellat, Talal Yassin Aleissa, Galb Shamailah, Ziad Alwahshat, Hashim Balas 
    Abstract: This study aims to explain the applicable legal system, which governs the conduct of transactions and contracts that apply according to the electronic commerce and electronic transactions system, through a statement of the law applicable to the relationship between the two parties under the Jordanian law and the law related to these issues governing electronic contracting operations. The research was carried out from a legal point of view based on the Jordanian laws related to this subject, the Jordanian Electronic Transactions Law, and with reference to the international conventions related to electronic commerce, which are centred on international business operations and international obligations on international electronic commerce, especially the two model laws. The most important findings are that the objective rules governing issues related to international electronic commerce contracts continue to play the role of completeness, allowing them to apply the conflict of laws approach to electronic commerce contracts.
    Keywords: electronic commerce contracts; law applicable; international contracts; Jordanian law.
    DOI: 10.1504/IJESDF.2025.10059959
  • A Proposed model for Security of Quality of Experience (QoE) Data in Cloud Gaming Environment
    by Awais Khan Jumani, Jinglun Shi, Asif Ali Laghari, Aftab Ul Nabi 
    Abstract: Gaming companies heavily rely on collecting quality of experience (QoE) data to gain insights into the performance of their games and make enhancements. This data encompasses aspects such as latency, packet loss and frame rate. By analysing QoE data companies can detect issues with the games infrastructure. Make adjustments to the games design. However, it is important to recognise that QoE data is also vulnerable to attacks. Hackers can exploit vulnerabilities in the games design. Because they are stealing data from QoE data. This unauthorised access can lead to troubles, for players or even compromise personal information of players. In this paper, we have evaluated security measures for online gaming and highlighted the issues related to the interplay between QoE data and data security, security threats, network attacks in multiplayer games, vulnerabilities in QoE data collection, and existing protocols. Moreover, we have proposed a new security measure for QoE data in a gaming environment. Our proposed measure uses encryption, access control, and intrusion detection to protect QoE data from unauthorised access and manipulation. In the last, we have underlined open research issues related to online gaming.
    Keywords: QoE security; online gaming; bidding; subjective QoE; objective QoE; online attacks.
    DOI: 10.1504/IJESDF.2025.10060310
  • An approach towards development of Supervisory Control and Data Acquisition System forensics framework: Concern and Challenges
    by Ramya Shah, Digvijaysinh Rathod 
    Abstract: In the highly competitive technology market, supervisory control and data acquisition/industrial control systems (SCADA/ICS) have seen quick growth. They are also at the heart of operational technology (OT), which is used in businesses and processing facilities to monitor and control crucial processes in varied sectors as energy, railways and many more. However, in the event of a security incident (such as a system failure, security breach, man-in-the-middle attack or denial-of-service attack), it’s critical to comprehend the digital forensics implications of such incidents, the procedures or protocols that must be followed during an investigation, the tools and techniques that an investigator should use, and where and how forensic data can be collected. It is crucial that forensic investigations start right away after a security incident due to the rising threat of sophisticated attacks on key infrastructures. This examination of current SCADA forensic researches and numerous forensic investigation methods is presented in this work. The limitations of employing conventional forensic investigative methods and the difficulties faced by forensic investigators have also been covered. The shortcomings of current research into offering forensic capacity for SCADA systems are also thoroughly reviewed.
    Keywords: SCADA forensics; ICS forensics; OT; digital forensics.

  • A DWT-DCT-SVD Based Robust Watermark Method for Image Copyright Protection
    Abstract: Due to advanced digitisation, the digital contents are easily spread via the internet and cause a lot of copyright concerns. To safeguard the copyright in the source image, watermarking focuses on covertly encrypting hidden data in the main image. This study presents a novel watermarking technique based on discrete wavelet transformation (DWT), discrete cosine transformation (DCT) and singular value decomposition (SVD). The approach first performs multilevel DWT on the cover image before applying the DCT on the low-frequency sub-band. Second, SVD is applied to the watermark image as well as the DCT coefficients of the source image. Thirdly, singular values from SVD operations are used to embed watermark information. In this approach, watermark identification and extraction are successfully done. Compared to the other methods, experimental outcomes show that the proposed method offers better performance in robustness and imperceptibility against different attacks.
    Keywords: image watermarking; discrete wavelet transformation; DWT; discrete cosine transformation; DCT; singular value decomposition; SVD.
    DOI: 10.1504/IJESDF.2025.10060617
  • A Forensic Approach: Identification of Source Printer through Deep Learning   Order a copy of this article
    by Kanica Chugh, Pooja Ahuja 
    Abstract: Forensic document forgery investigations have elevated the need for source identification for printed documents during the past few years It is necessary to create a reliable and acceptable safety testing instrument to determine the credibility of printed materials The proposed system in this study uses a neural network to detect the original printer used in forensic document forgery investigations The study uses a deep neural network method which relies on the quality, texture, and accuracy of images printed by various models of Canon and HP printers The datasets were trained and tested to predict the accuracy using logical function, with the goal of creating a reliable and acceptable safety testing instrument for determining the credibility of printed materials The technique classified the model with 95.1% accuracy The proposed method for identifying the source of the printer is a non-destructive technique.
    Keywords: forensic document analysis; printed documents; deep learning; convolutional neural network; CNN; printer identification.
    DOI: 10.1504/IJESDF.2025.10062209
  • IoT Security: A Systematic Literature Review of Feature Selection Methods for Machine Learning-based Attack Classification   Order a copy of this article
    by Jing Li, Mohd Shahizan Othman, Chen Hewan, Lizawati Mi Yu 
    Abstract: In the age of the internet of things (IoT), ensuring security is crucial to protect the interconnected devices and systems. The capacity to identify cyberattacks is essential for IoT security, hence many academics have focused their efforts on developing powerful classification models that can identify intrusions to protect IoT infrastructure. One key factor in creating successful classification models for IoT security is feature selection. To assist researchers and practitioners in selecting the appropriate feature selection methods, this paper presents a systematic literature review of the literature on feature selection approaches for machine learning-based attack classification models in IoT security using IoT datasets. By analysing data from 1272 studies published between January 2018 and December 2022 using preferred reporting items for systematic literature reviews and meta-analyses (PRISMA) guidelines, the review identifies 63 primary studies that meet inclusion criteria. The primary studies are analysed and categorised to answer research questions related to current practices, feature selection methods, benchmark IoT datasets, feature selection validation methods, limitations, challenges, and future directions. The review provides valuable insights for researchers and practitioners seeking to incorporate effective feature selection approaches in IoT security.
    Keywords: internet of things; IoT; feature selection; FS; IoT dataset; attack detection; classification; IoT security; systematic literature review; SLR.
    DOI: 10.1504/IJESDF.2024.10060679
  • A Hybrid Approach for Social Media Forensics
    by Amir Shaker Mahmoud, Ngaira Mandela, Animesh Kumar Agrawal, Nilay Mistry 
    Abstract: Social media plays a pivotal role as a rich source of data for digital forensics investigators, enabling the extraction of valuable evidence for crime analysis. This research introduces a hybrid digital forensics approach for social media investigation, compatible with all web-browsing devices. This hybrid approach combines a three-stage classification process consisting of TextBlob sentiment analysis to analyse the tweet’s polarity, VADER to detect suspicious tweets, and a crime-type dictionary to categorise crime-related tweets. Using Python libraries, Twitter data is collected using authentic Twitter accounts, hashtags, or keywords, then cleaned, translated, geolocated, and classified. A real-time interactive platform is implemented for crime detection and analysis, bolstering the capabilities of law enforcement agencies and researchers in understanding crime patterns. The research concludes with promising results, highlighting the approach’s potential, and discusses future enhancements, ultimately aiding in crime analysis and prevention.
    Keywords: crimes analysis; cybercrimes; digital forensics; hybrid methods; text blob; VADER; social network analysis; Twitter; law enforcement.
    DOI: 10.1504/IJESDF.2025.10060756
  • A Secure SEO Techniques for Improving the Website Ranking: An Efficient Approach
    by Muhammad Abbas, Sarmad Ahmed Shaikh, Rumsia Tahir, Abdullah Ayub Khan, Asif Ali Laghari 
    Abstract: This research paper provides an in-depth exploration of the use of search engine optimisation (SEO) techniques as a means of enhancing a website’s ranking on search engine results pages (SERPs). In the current digital era, where a website’s visibility can make or break its success, SEO has become a crucial component of digital marketing strategies. Therefore, this study focuses on applying SEO strategies to a tech-related website with the aim of achieving a certain level of ranking and targeting specific audiences. They identify several key elements that can improve search results, including on-page optimisation, link building, and keyword research. Therefore, it is essential to employ a holistic approach to SEO that considers various factors, such as the website’s content, structure, and user experience. The paper concludes by examining the concept of optimisation in the context of a particular website, highlighting the importance of continuous monitoring and adaptation of SEO strategies. Overall, they provide valuable insights into the world of SEO and its potential to enhance a website’s visibility and success.
    Keywords: search engine optimisation; SEO; KGR; on-page optimisation; off-page optimisation.
    DOI: 10.1504/IJESDF.2025.10061127
  • A novel method to increase the security in 5G networks using Deep Learning
    by Rajasekar A, Ramamoorthi R., RAMYA M, Vinod A 
    Abstract: Wireless networks are being forced to handle a greater amount of data due to a variety of circumstances, and this trend is progressing at a rapid rate. The Denial of Service (DoS) attacks have the highest rate of growth target the expanding computational network infrastructures all over the globe. As a consequence of this, the objective of this study is to come up with an innovative model for the identification of DoS attacks on 5 G networks. The model will go through two phases: the first will be feature extraction, and the second will be attack detection. In order to successfully carry out the detection, classifiers that have long short-term memories (LSTM) are utilised. The Whale Optimization Algorithm (WOA) model works to optimise the weight of the LSTM. The detected output of a hybrid model that has been trained appropriately provides the accuracy rate of 96.5%
    Keywords: 5G; Deep learning; Dos attacks; Denial-of-service; whale optimization algorithm.
    DOI: 10.1504/IJESDF.2025.10061148
  • The Future of Third Web: A Role of Blockchain and Web 3.0
    by Usman Ali, Irfan Ali Kandhro, Raja Sohail Ahmed Larik, Abdullah Ayub Khan, Muhammad Huzaifa Shahbaz, Muhammad Osama 
    Abstract: Nowadays, people use the web more consistently and the world wide web (www) is used as the largest global information media by this user can write, read or share information throughout the internet. The early web tools were very simple. But with time new tools emerge. The first version of www was web 1.0 which was only static, and the second version of www is web 2.0 users can only read, write and create the data which is help businesses to cover the dynamic data of the users. But the third version of www is web 3.0 use the algorithm which works differently for every user to interpret the individual data and customize the internet for every user. Many companies like YouTube, Netflix, and Spotify use this technology and only share valuable things with users by analyzing their data and behavior.
    Keywords: World Wide Web (WWW); blockchain; Web 3.0; Information System; Risks; Privacy and Security.
    DOI: 10.1504/IJESDF.2025.10061149
  • Legal Regulation of Payment Using Virtual Currency: Comparative Study
    by Nahed Alhammouri, Ali Al-Hammouri, Tareq Al-billeh, Abdulaziz Almamari 
    Abstract: The article analyses the legal regulation of payment using virtual currency, so that virtual currencies such as Bitcoin and Ethereum deal with online payments and transfers directly without relying on traditional financial institutions. The study therefore focuses on the framework for the legal regulation of these currencies and their impact on the economy and consumers. The study includes a review of current legislation in different countries governing the use and trading of virtual currencies. It also examines legal and security challenges related to this type of payment, including fraud, money laundering and funding terrorism. The study highlights the central bank’s and governments’ efforts to regulate and control the use of virtual currencies, including licensing common platforms and exchanges and restricting investment and transfer operations. The study emphasises the need for balanced regulation to achieve legal protection for stakeholders without prejudice to the innovation and development of financial technology.
    Keywords: virtual currency; electronic payment; legal challenges; online transfer; financial technology; international contracts.
    DOI: 10.1504/IJESDF.2025.10061361
  • Secure sensing and computing techniques based on fuzzy in 5G   Order a copy of this article
    by Ramesh Balasubramani, Kumarganesh Sengottaiyan, Thillaikkarasi Rangasamy, Susaritha Muthusamy , Elango Sellamuthu, Mahaboob Basha Shaik 
    Abstract: The new technology that is employed in 5G applications presents potential security vulnerabilities. In this study, the security needs for 5G applications are analysed, and hierarchical solutions for securing 5G apps are presented for various stakeholders. This study analyses the process of vulnerability assessment in 5G networks and offers an optimised dynamic technique for precisely analysing the vulnerabilities that exist in 5G networks. Specifically, the research focuses on how 5G networks may be attacked. Combining fuzzy numbers with the method for order of preference by similarity to the ideal solution (TOPSIS) is what this approach does. The proposed technique considers both static and dynamic aspects of the 5G network, such as latency and accessibility, to discover the possible attack graph pathways along which an attack may spread across the network. In addition, we compare the enhanced technique to both the classic TOPSIS and the widely used susceptibility scanning application known as Nessus.
    Keywords: fuzzy; security; 5G; encryption algorithms; internet of things; IoT; 5G heterogeneous networks.
    DOI: 10.1504/IJESDF.2025.10061442
    by Kiran Shrimant Kakade, Om Astankar Astankar, Anjali Kulkarni, Jayant Brahmane, Sulakshana B. Mane, Poonam Nathani 
    Abstract: Misuse of digital platforms by people especially at tender age and their behaviour in the virtual world is a cause of concern for various regulating agencies. This study gathers the most recent and available evidences about the relation between behavioural aspects of people in cyber world along with their inclination towards cyber-crime. It was specifically created to understand the demographic characteristics that contribute to cybercrime through a variety of mixed analytical methodologies and the incorporation of theoretical frameworks from criminology and psychology, including cyber psychology and computer science. The possibility that young people with an interest in technology could develop into cyber-juvenile offenders, lone cyber-criminals, and organised cyber-criminals was taken into account. Understanding whether demographic factors also influences cybercrime is essential for developing effective prevention and intervention strategies. The results of the study are expected to provide guidance to the regulating agencies as well as other organisations to design measures for preventing cyber-crime and ensure safety of their system and processes from the danger of cyber criminals.
    Keywords: cybercrime; juvenile delinquency; generation Z; cyber world; iGeneration; India.
    DOI: 10.1504/IJESDF.2025.10061443
  • DarkExtract: Tool for Extracting and Analyzing Tor browser host-based Activities   Order a copy of this article
    by Ngaira Mandela, Amir Shaker Mahmoud, Animesh Kumar, Nilay Mistry 
    Abstract: The increasing usage of Tor Browser, a popular tool for anonymous web browsing, has presented unique challenges for forensic investigators in analysing digital evidence. This research paper introduces Dark_Extract, an open-source tool designed to simplify the identification and analysis of host-based artefacts left by Tor browser. The purpose of this study is to address the challenges associated with forensic analysis of Tor Browser traces by providing a user-friendly and efficient solution. The methodology employed in developing Dark_Extract involved the analysis of Tor Browser’s architecture and the identification of key host-based artefacts relevant to forensic investigation. The tool was then developed to automate the extraction and analysis of these artefacts, eliminating the need for extensive knowledge of Tor Browser’s intricate structure. The major findings of this study demonstrate the effectiveness of Dark_Extract in simplifying the forensic analysis of Tor Browser traces. The tool successfully extracts and presents crucial host-based artifacts such as downloads, cookies, browsing history, and bookmarks, which can be of significant importance in forensic investigations. The results obtained through the use of Dark_Extract indicate its accuracy and efficiency in identifying and organising these artefacts.
    Keywords: Tor browser; dark web; dark net; forensic investigation; digital evidence; host-based artefacts; anonymous web browsing; forensic data extraction.
    DOI: 10.1504/IJESDF.2025.10061873
  • VLMDALP: Design of an efficient VARMA LSTM based Model for identification of DDoS attacks using Application-Level Packet analysis   Order a copy of this article
    by Meghana Solanki, Sangita Chaudhari 
    Abstract: Identification of distributed denial-of-service (DDoS) attacks at the application level in networks is a multimodal task that involves the analysis of various network parameters, including packet signatures, node-level analysis, and traffic patterns. Existing attack detection models are highly complex or do not support multiple attack scenarios. To address these issues, we propose an efficient hybrid model that combines the Vector Autoregressive Moving Average (VARMA) with Long Short-Term Memory (LSTM) techniques for identifying DDoS attacks through application-level packet analysis. The proposed model initially employs the Vector Autoregressive Moving Average Model to extract hierarchical features from raw packet data across multiple domains, including time, frequency, and spatial domains. These learned features are then enhanced using the LSTM model. The combined features create a concise and informative representation of the packets, which is fed into a fully connected neural network for classifying multiple types of attacks. To evaluate the effectiveness of our proposed model, we conducted experiments on real-world network datasets, including samples from the Application- Layer DDoS Dataset.
    Keywords: Network Forensics; Attacks; Analysis; Application Layer DDoS; VARMA; LSTM; Samples.
    DOI: 10.1504/IJESDF.2025.10061885
  • Advanced forensic analysis of Tails Operating Systems and its implication to cybercrime in Deep and Dark web   Order a copy of this article
    by Ngaira Mandela, Amir Shaker Mahmoud, Animesh Kumar, Nilay Mistry 
    Abstract: The Tails operating system, renowned for its emphasis on privacy and anonymity, has become a preferred choice for individuals seeking to safeguard their online activities. Tails OS design centres around providing a secure environment that leaves minimal traces, thereby attracting privacy-conscious users. However, this very secure design also entices cyber criminals operating in the digital landscape, to use Tails to perpetrate illicit activities, creating new challenges for digital forensics practitioners in their pursuit of extracting evidence. This paper conducts an exhaustive forensics of Tails operating system, aiming to uncover the digital remnants left behind during its utilisation. By analysing the RAM, network, disk, and virtualisation, employing a range of activities and forensic tools, many artefacts are unearthed that provide insight into user interactions within the Tails environment. This research contributes to our understanding of the interplay between privacy preservation and digital evidence recovery, shedding light on the complexities of investigating a privacy-focused operating system like Tails.
    Keywords: Tails operating system; amnesic incognito live system; privacy-focused OS; anonymity; cybercrime; digital forensics; deep and dark web.
    DOI: 10.1504/IJESDF.2025.10061945
  • Forensic Investigation and Analysis of Malware in Windows OS   Order a copy of this article
    by Frank Fiadufe, Krishna Modi, Kapil Shukla, Felix O. Etyang 
    Abstract: Malware has become a pervasive concern for malware analysts and digital forensics. This research investigates malware forensics to detect, investigate, and analyse malicious software. The research examines the application of digital forensic science to dissect threat vectors, specifically malware, shedding light on their behaviour on computer hard disks and memory. Using various digital forensic tools, memory forensics, and harddisk forensics are performed on an infected Windows 7 OS, followed by static and dynamic analysis of malicious software. Memory samples are analysed using volatility for memory forensics, while disk images are analysed using autopsy for harddisk forensics. The malware’s functionality is fully comprehended through meticulous extraction and analysis. A robust framework for malware forensic investigation emerges, facilitating detection, analysis, and understanding of malware behaviour. This research underscores the significance of integrating digital forensics tools and techniques to combat evolving malware threats effectively.
    Keywords: digital forensics; memory forensics; harddisk forensics; static and dynamic analysis.
    DOI: 10.1504/IJESDF.2025.10062010
  • Network Security Attack Classification: Leveraging Machine Learning Methods for Enhanced Detection and Defense.   Order a copy of this article
    by Irfan Ali Kandhro, Ali Orangzeb Panhwar, Shafique Ahmed Awan, Raja Sohail Ahmed Larik, Abdul Ahad Abro 
    Abstract: The rapid growth and advancement of information exchange over the internet and mobile technologies have resulted in a significant increase in malicious network attacks. Machine learning (ML) algorithms have emerged as crucial tools in network security for accurately classifying and detecting these attacks, enabling effective defence strategies. In this paper, we employed ML methods such as logistic regression (LG), random forest (RF), decision tree (DT), k-nearest neighbours (KNN), and support vector machines (SVM) for building an intrusion detection system using the publicly available NSL-KDD dataset. Our proposed method utilised feature engineering and selection techniques to extract relevant features. We trained classification models and optimised their parameters using cross-validation and grid search techniques. The models exhibited robustness in identifying unseen attacks, enabling proactive defence strategies. In this paper, we contribute to the field of network security by showcasing the efficacy of machine learning methods, empowering organisations to enhance their defences and respond to threats promptly. Future research can explore advanced models and real-time monitoring techniques to develop dynamic defence mechanisms.
    Keywords: attacks classification; network security; cyber security; machine learning; adversarial attacks.
    DOI: 10.1504/IJESDF.2025.10062253
  • A model for detecting cyber security intrusions using machine learning techniques   Order a copy of this article
    by Leo John Baptist, Janani Selvam, Divya Midhun Chakkaravarthy 
    Abstract: Because hackers are using more sophisticated methods, the number of cyberattacks is rising at an alarming rate. In addition, maintaining adequate levels of cyber security is becoming more difficult on a daily basis due to the prevalence of malicious actors carrying out cyberattacks in the modern digital environment. Therefore, in order to have a safe network, it is required to establish privacy and security measures for the systems. A substantial amount of further research is required in the domain of intrusion detection. This study introduces an intrusion detection tree (referred to as ‘IntruDTree’), which is a security model based on machine learning. Ultimately, the efficacy of the IntruDTree model was assessed by the execution of tests on many cybersecurity datasets. To assess the efficacy of the resulting security model, we conduct a comparative analysis between the outputs of the IntruDTree model and those of other well-established machine learning techniques.
    Keywords: cybersecurity; cyber-attacks; anomaly detection; intrusion detection system; machine learning; ML.
    DOI: 10.1504/IJESDF.2025.10062655
  • Artificial intelligence and security: some reflections concerning the freedom of expression, information and democracy   Order a copy of this article
    by Federico Fusco 
    Abstract: In contemporary society, the role of information in socio-economic development is increasing across domains such as policy, business, technology, and society. Among sources of information, news hold significant sway in shaping public opinion. However, the proliferation of fake news presents a significant threat to many societies, particularly when they are part of disinformation campaigns orchestrated by hostile actors. While disinformation campaigns are not a new phenomenon, the ease and speed of spreading false information via the internet and social media, as well as advancements in artificial intelligence that enable text generation resembling human language, have made them a growing concern. In light of these developments, this paper provides a comprehensive examination of the challenges posed by fake news, with a particular focus on the role played by artificial intelligence.
    Keywords: artificial intelligence; fake news; freedom of expression; disinformation; media law; democracy.
    DOI: 10.1504/IJESDF.2025.10062899
  • IoT security using deep learning algorithm: intrusion detection model using LSTM   Order a copy of this article
    by Abitha V. K. Lija, R. Shobana, J. Caroline Misbha, S. Chandrakala 
    Abstract: Internet of things (IoT) and the integration of many gadgets is rapidly becoming a reality. IoT devices, particularly edge devices, are particularly vulnerable to cyberattacks as a result of the proliferation of device-to-device (D2D) connectivity Advanced network security measures are required to do real-time traffic analysis and to mitigate malicious traffic. These mechanisms must also be able to detect malicious traffic. We describe a game-changing approach to detect and classify new malware in record time. This will allow us to handle the difficulty that has been presented (zero-day malware). This article puts out the idea of a hybrid deep learning (DL) model for the detection of cyber attacks. Long short-term memory (LSTM) and gated recurrent unit are the foundations of the model that has been suggested (GRU). The results of the experiments are quite encouraging, revealing an accuracy rate of 94.50% for the identification of malware traffic.
    Keywords: deep learning; gated recurrent units; internet of things; IoT; long short-term memory; LSTM; machine learning.
    DOI: 10.1504/IJESDF.2025.10063216
  • A novel scalable and cost efficient blockchain solution for managing lifetime vaccination records based on patient preference   Order a copy of this article
    by Neetu Sharma, Rajesh Rohilla 
    Abstract: This study aims to design a novel, cost-efficient blockchain-based solution for managing lifetime vaccination records based on patient preference. The proposed design reduces fraud in vaccination certification by providing QR code-based validation. The proposed system stores the cryptographic-hash of vaccination certificates on the blockchain for security and integrity validation. For scalability, availability, and store-house cost reduction, vaccination records have been stored off-chain through a private interplanetary file system (IPFS) based on patient preference. The smart contract is successfully deployed and tested over the Remix IDE environment. Performance has been evaluated by analysing execution costs at different transaction sizes. Moreover, we have evaluated the probability of data availability for a private IPFS network, which was not done in any previous work. Furthermore, we have analysed the network parameters to get optimal data availability at a low storage cost. The comparative analysis proves that the proposed scheme is better than existing schemes.
    Keywords: blockchain; vaccination; security; IPFS; scalable; Ethereum; smart contract.
    DOI: 10.1504/IJESDF.2025.10063362
  • A proposed framework for the detection of cyber threats using open-source intelligence tools in real-time   Order a copy of this article
    by Ravi Sheth, Chandresh Parekha 
    Abstract: In today’s fast-changing digital world, getting accurate and relevant information is crucial for cybersecurity, competitive analysis, and research. With open-source intelligence (OSINT) technology, social media and digital platforms have become significant data sources. Our research will compare different OSINT systems for data collection. We’ll examine various OSINT tools, categorise them based on functionality, usability, and effectiveness, including web scrapers, social media analytics, domain analysers, and search engines. Each tool will undergo evaluation based on data collection capabilities, result accuracy, module availability, and user-friendliness. It aims to assist individuals - be they sufferers, academics, or practitioners in making informed decisions when selecting open-source intelligence (OSINT) tools for information extraction. We have provided a detailed comparison of their capabilities and limitations, along with ethical considerations, for using OSINT tools effectively and responsibly in various applications and the proposed cyber threat alert system in this paper.
    Keywords: template OSINT; information gathering; web scanner; search engine; cyber security.
    DOI: 10.1504/IJESDF.2025.10064154
  • Image encryption and decryption using graph theory   Order a copy of this article
    by Selva Kumar, Saravanakumar Chandrasekaran, Nalini Manogaran, Bhadmavadhi Krishna 
    Abstract: Through the application of the ideas presented in graph theory, this work presents a novel approach to the protection of picture data. The approach that has been developed takes into account the pixels that make up the digital picture as vertices of a network and forms edges between the vertices, assigning a certain amount of meaningful weight to each connection. The encryption and decryption procedure for the colour digital picture is presented. This approach makes use of the minimum spanning tree (MST) and the weighted adjacency matrix of the MST. For the purpose of validating the practicability and robustness of the suggested approach, the experimental findings and the security analysis of the proposed methodology are presented. In order to demonstrate that the suggested approach is resistant to statistical assaults, statistical analysis techniques such as histogram, correlation, and entropy were used. It has also been shown via the results of the experiments that the suggested method is resistant to assaults including brute force and occlusion.
    Keywords: image encryption; graph theory; encryption phase; computation results.
    DOI: 10.1504/IJESDF.2025.10064183
  • Adversarial attacks on machine learning-based cyber security systems: a survey of techniques and defences   Order a copy of this article
    by Pratik S. Patel, Pooja Panchal 
    Abstract: Machine learning (ML) has been increasingly adopted in the field of cyber security to enhance the detection and prevention of cyber threats. However, recent studies have demonstrated that ML-based cyber security systems are vulnerable to adversarial attacks, in which an attacker manipulates input data to deceive the ML model and evade detection. This paper presents a survey of adversarial attacks on ML-based cyber security systems, including techniques such as evasion, poisoning, and backdoor attacks. Additionally, we discuss the limitations of current defences against adversarial attacks, such as defensive distillation and adversarial training, and propose potential future directions for defence mechanisms. Finally, we provide a framework for evaluating the effectiveness of existing defences against adversarial attacks on ML-based cyber security systems. Our survey highlights the urgent need for developing more robust and reliable defence mechanisms to ensure the security and reliability of ML-based cyber security systems in the face of adversarial attacks.
    Keywords: attacks; machine learning; ML; cybersecurity; evasion; threat.
    DOI: 10.1504/IJESDF.2025.10064222
  • An intelligent method for detection and classification of Darknet traffic using sequential model along with Adam and stochastic gradient decent optimisers   Order a copy of this article
    by Ravi Sheth, Chandresh Parekha, Kinjal Sheth 
    Abstract: The clandestine nature of darknet activities poses a significant challenge to traditional cybersecurity measures, necessitating advanced techniques for effective detection and classification. Darknet traffic classification is very much needed now days as day by day the market of illegal and hidden services are being increased in the darknet. There are various machine learning-based approach has been proposed for the categorisation of darknet traffic but very few work has been done using the concept of deep learning. This research introduces an intelligent approach which leverages a sequential deep learning model to enhance the accuracy and efficiency of darknet traffic detection and classification. In the training phase, the model is exposed to a diverse dataset encompassing a wide range of darknet traffic patterns, ensuring its ability to generalise and recognise novel patterns in real-world scenarios. The proposed model has used sequential model along with the stochastic gradient descent (SGD) and Adam optimiser which successfully detect and classify the darknet traffic with the overall accuracy of 96.77%.
    Keywords: sequential model; Adam; stochastic gradient decent; SGD; darknet traffic; detection; classification.
    DOI: 10.1504/IJESDF.2025.10064345
  • On classifying memory contents at page-level granularity: machine-learning approach   Order a copy of this article
    by Mohammed I. Al-Saleh, Akram Alkouz, Abdulsalam Alarabeyyat, Majed Bouchahma 
    Abstract: A significant challenge faced by digital investigators in the realm of law is performing digital media triage, which involves determining the relevant data that may aid in a criminal investigation. Effective triage can save time and improve investigative outcomes, particularly in memory investigation, as its contents are often scattered and diverse. Identifying and classifying file types in memory can be difficult due to the way the operating system’s paging scheme maps file contents into non-consecutive page frames in physical memory. This paper presents a machine learning approach to triage memory content at the page level, focusing on the classification of common file types within the context of law. The study conducted various experiments, and the results indicate that it is possible to accurately classify in-memory data into primary file categories, thus contributing to the field of digital investigation in accordance with legal processes.
    Keywords: memory forensics; digital media triage; machine learning; file type detection; digital forensics; classifying memory content.
    DOI: 10.1504/IJESDF.2025.10064346
  • Enhancing network security: a deep learning-based method to detect and diminish attacks   Order a copy of this article
    by R.E. Franklin Jino, Arockia Mary Paulsamy, Gobinath Shanmugan, Rajesh Kumar Vishwakarma 
    Abstract: In today’s world, preventing illegal intrusions into communication networks is an absolute need in order to protect the personal information of users and maintain the integrity of their data. The establishment of intrusion detection systems (IDS) and the improvement of their accuracy have both been shown to benefit from the use of data mining and machine learning techniques. We make use of the well-known AWID3 dataset, which contains traffic from wireless networks. The Krack and Kr00k attacks, which are aimed at the most serious vulnerabilities in the IEEE 802.11 protocols, are the primary focus of our research and development efforts. This success rate was reached by our ensemble classifier. When it came to identifying instances of the Kr00k attack, our neural network-based model had a high accuracy rate of 96.7%, which further emphasised the usefulness of the remedy that we suggested.
    Keywords: wireless; IDS; machine learning; Krack; Kr00k; IEEE8021.11.
    DOI: 10.1504/IJESDF.2025.10064402
  • Exploring novel encryption approaches for safeguarding heterogeneous data   Order a copy of this article
    by Dhruvi Karansinh Zala, Mohammad Akram Khan 
    Abstract: In today’s global landscape, data security faces complex challenges due to diverse data forms and transmission structures. This study explores safeguarding data through unconventional encryption methods, stressing the need for adaptable techniques. It begins by assessing traditional encryption’s performance with diverse data types, highlighting security gaps in uniform encryption. The research advocates for encryption that handles data complexities, proposing strategies aligned with heterogeneous data’s variability. Practical tests and simulations inform these novel encryption algorithms, aiding in advancing security protocols for diverse scenarios. Ultimately, this research contributes to evolving data security in heterogeneous environments, emphasizing the importance of robust encryption in managing varied data formats and structures in an interconnected world.
    Keywords: encryption; data security; image; audio; video; text; heterogeneous data; privacy preservation; hybrid encryption; multi-level encryption.
    DOI: 10.1504/IJESDF.2025.10064447
  • Machine learning-based cyber attack recognition model   Order a copy of this article
    by Leo John Baptist, Janani Selvam, Divya Midhun Chakkaravarthy 
    Abstract: Internet plays an essential role in the daily lives of individuals living in the contemporary world. Because of the volume of users, our private information runs the risk of being disclosed inadvertently somewhere else on the internet. The study of cyber security encompasses a wide range of topics, the most basic of which are the abuse of data and risks to internet security. The proposed system that performs an analysis of the dataset and determines if the data in question is typical or out of the ordinary. Following the completion of the dataset analysis, make an effort to recognise and forecast a cyber attack. The ensemble classification approach is used to determine the attack wise detection accuracy found by CADM. The categorisation of network traffic data has been done with the help of the gradient boosting and random forest algorithms. We achieved an accuracy level of 97.4%.
    Keywords: cyber attack detection; deep machine learning; DML; smart power grid; data processing.
    DOI: 10.1504/IJESDF.2025.10064475
  • Machine learning in IoT digital forensics: a state-of-the-art review   Order a copy of this article
    by Abdullah Aldhayyaf, Samer Atawneh, Bushra Abdullah Shtayt 
    Abstract: Today, the pace of development of IoT devices has increased, which has led to their great use in various fields; therefore, ensuring their security against potential threats has become very important with the expansion of the IoT ecosystem. Digital forensics helps in investigating and preventing cybercrimes. IoT forensics focuses on identifying the attack's origin on the IoT devices and networks. Machine learning algorithms can process vast amounts of data and identify cyberattacks that might go unnoticed by traditional methods. By analysing data from IoT devices, these techniques can help in the early detection and prevention of cyber threats. This research seeks to explore the role of machine-learning methods in IoT digital forensics by providing a comprehensive look at the studies that dealt with this topic from multiple perspectives in terms of the machine-learning techniques and applications in IoT forensics. Anomaly detection will also be discussed in this paper.
    Keywords: internet of things; IoT; digital forensics; DF; machine learning; ML; deep learning; DL; anomaly detection; security.
    DOI: 10.1504/IJESDF.2025.10064547
  • Exploring machine learning techniques for detecting anomalies in digital forensics: a survey   Order a copy of this article
    by Khawla Almutawa, Afef SELMI, Tarek Moulahi 
    Abstract: Cybercrime has recently and rapidly increased as a result of the extensive use of various digital devices. Digital forensic science, which was established to address issues of cybercrime, follows a defined approach to gathering digital evidence. In recent years, there has been a growing number of studies focusing on employing machine learning and deep learning in digital forensics applications. This research is motivated by the increasing importance of digital forensics and cybersecurity and the need for accurate and efficient tools to detect and mitigate potential security breaches and other forms of anomalous behaviour in digital systems. The purpose of this study is to conduct a literature review to obtain a comprehensive understanding of this topic, specifically deployed models, data preprocessing mechanisms, anomaly detection techniques, and evaluations. This review will help to identify gaps in the existing knowledge and potentially uncover new approaches to the topic. This review conducts a comprehensive literature review on anomaly detection in log files using ML and DL techniques. It will help to identify gaps in the existing knowledge and potentially uncover new approaches to the topic. Initial results indicate that deep learning methods show promise in effectively dealing with the intricate characteristics of log data.
    Keywords: digital forensics; DF; machine learning; ML; anomaly detection; log files.
    DOI: 10.1504/IJESDF.2025.10064574
  • Deep learning-based anomaly detection in video surveillance   Order a copy of this article
    by C. Rajesh, B. R. Tapas Bapu , S. Asha , Ravi Kishore Veluri 
    Abstract: In this day and age of smart cities, the use of video monitoring has assumed a position of critical significance. Large numbers of surveillance cameras have been installed in public and private locations for the purpose of monitoring the properties of infrastructure and ensuring the safety of the general public. This research provides a multi-modal CNN-BiLSTM autoencoder framework for detecting anomalous events in important surveillance environments such as bank ATMs. The approach is built on semi-supervised deep learning and uses multi-modal data. In addition, because there was no publicly accessible dataset for ATM surveillance, we created a one-of-a-kind RGB+D dataset specifically for this purpose. This was done because there was no dataset for ATM surveillance in the public domain. The proposed methodology is validated by testing it on the RGB+D dataset that was collected as well as two other real-world benchmark video anomaly datasets: Avenue and UCFCrime2Local.
    Keywords: video surveillance; security; deep learning algorithm; anomaly detection.
    DOI: 10.1504/IJESDF.2025.10064692
  • A supervised machine learning based framework for deanonymisation of blockchain transactions   Order a copy of this article
    by Rohit Saxena, Deepak Arora, Vishal Nagar 
    Abstract: As a cryptocurrency, Bitcoin serves as a decentralised ledger for recording transactions. The owner of a Bitcoin keeps their identity secret and hides it behind a special address known as a pseudonym. Because Bitcoin offers anonymity, it has evolved into the favoured option for cybercriminals involved in illegal activities. In this research, supervised machine learning has been used to propose a framework for identifying anonymous user activities on the Blockchain. A labelled dataset containing transactions has been created as a training dataset to carry out the classification of user activities. The fundamental objective is to classify Blockchain transactions to deanonymise them and separate unethical from ethical ones. Synthetic minority oversampling technique (SMOTE) and weight of user activities were used to address the issue of class imbalance. On the samples from the class imbalanced and class balanced datasets, k-nearest neighbours (KNN) exhibited outstanding cross-validation accuracy with default parameters and hyperparameters.
    Keywords: Bitcoin; deanonymisation; supervised machine learning; classification; k-nearest neighbours; KNN; decision trees.
    DOI: 10.1504/IJESDF.2025.10064694
  • AI chatbots: security and privacy challenges   Order a copy of this article
    by Manju Lata, Vikas Kumar 
    Abstract: The increasing use of artificial intelligence (AI) chatbots in different domains has surely developed the competence, customer capabilities and engagement. On the other hand, the AI chatbots regularly handle sensitive data, making them striking objects on behalf of the malicious actors. Present work describes the prominent security and privacy issues related to the use of AI chatbots, in order to plan the mitigation strategies. An all-inclusive approach is required to handle the security and privacy issues to ensure the transparent data practices, robust security techniques, ethical improvement procedures, and compliance with significant regulations. Since the AI chatbots continue to advance at a faster pace, a pre-emptive approach is crucial for reliable and protected amalgamation into sustainable digital lives. By probing and mitigating security and privacy challenges, this paper contributes to the reliable development and deployment of AI chatbots, with adoption of more secure and trustworthy conversational AI environment.
    Keywords: AI chatbots; security; privacy; data security; mitigation strategies; challenges; AI environment.
    DOI: 10.1504/IJESDF.2025.10065035
  • Data security by means of cryptography and image processing - a deep learning based method   Order a copy of this article
    by Pooja Sharma, Anurag Patel 
    Abstract: Traditional picture encryption methods make use of rounds of diffusion and confusion in order to create an explicit trade-off between the degree of security and the length of time it takes to decode an image. Deep learning is a method that has the potential to be used in the production of responses that are acceptable for present problems in photo encryption systems. We explore the advantages and disadvantages of each of these research approaches and develop conclusions that are relevant to our findings. In the second step of the process, the various methods are compared and analysed in terms of the cryptographic properties of the recovered photos and the quality of the cipher images that are produced. Finally, conclusions are formed from comparing and analysing the deep learning technique in end-to-end encryption and decryption systems. These findings serve as a platform for future research.
    Keywords: deep learning; image encryption; cryptographic attacks; encryption keys; style transfer.
    DOI: 10.1504/IJESDF.2025.10065036
  • Matrix-based homomorphic encryption-using random prime numbers   Order a copy of this article
    by Sonam Mittal, Ketti Ramachandran Ramkumar 
    Abstract: Cloud service providers (CSPs) provide security to data during communication and storage, but data security during computation remains a challenge. Homomorphic encryption facilitates the computation of the encrypted data to guarantee the confidentiality and security of the client’s data. Most of the existing homomorphic encryption schemes work on bit-level plaintext and have various issues, such as large ciphertext size, impractical key size, noise growth, and more computational overhead. etc. The paper presents a homomorphic encryption algorithm based on integer numbers which uses the camouflage process and a matrix, filled with random prime numbers to transform the original plaintext to the next prime number and to have a more secure encryption scheme with reduced computational and memory overheads. The comparative analysis shows the memory requirement, ciphertext size, and key length as 4,896 bytes, 16.7 digits, and 3.6 digits for variant 1 and 4,855 bytes, 17.1 and 3.5 digits for variant 2 are less than existing standards.
    Keywords: homomorphic encryption; security; random prime numbers; substitutional matrix; camouflage process; cloud service providers; CSPs.
    DOI: 10.1504/IJESDF.2025.10065110
  • Enhancement of data security in wireless sensors network: application in internet of things   Order a copy of this article
    by K. Sathiya Priya, C. Rajabhushanam 
    Abstract: As wireless sensor networks (WSNs) expand their application areas and become more widely utilised, the need for security in these networks becomes unavoidable and essential. The fundamental properties of wireless sensor networks do, however, impose limits on sensor nodes. These constraints include limited energy, processing capabilities, and storage capacity, among other things. The purpose of this article is to offer an outline of the problems about privacy and security that are linked with sensor networks. It has been shown that asymmetric key cryptography is not suitable for wireless sensor networks. However, in light of the development of new energy-efficient sensor nodes such as TelosB and others, researchers are exploring and assessing the influence that public key cryptography has on wireless sensor networks. At the time of data transmission, the proposed work achieves a 9.67% improvement in security, and it achieves an 11.38% improvement in security.
    Keywords: secure data aggregation in WSN; concealed data aggregation; homomorphic encryption.
    DOI: 10.1504/IJESDF.2025.10065111
  • Image encryption using artificial intelligence algorithms for secure communication   Order a copy of this article
    by S. Kumarganesh, D. Jennifer, B. Ramesh, S. Elango, A. Gopalakrishnan 
    Abstract: Within the context of today’s networks and massive amounts of data, the safe transmission of digital photographs is met with a myriad of formidable obstacles. We provide an adaptable framework with the goal of maintaining the privacy and safety of photographs that are delivered over an electronic healthcare system. In our technique, the 3D-chaotic system is used to produce a keystream, which is then applied to the picture to achieve 8-bit and 2-bit permutations respectively. The efficacy of the proposed encryption method is shown using histogram analysis, neighbouring pixel correlation analysis, anti-noise attack analysis, and resistance to occlusion attack analysis. The technique of encryption offers a number of benefits, including a high volume of information that may be encrypted, strong resilience, and a quick decryption time. This demonstrates that the scheme is capable of withstanding statistical attacks and suitable for use as a security framework in AI-based healthcare.
    Keywords: image encryption; AI; healthcare; secure communication.
    DOI: 10.1504/IJESDF.2026.10065276
  • Constitutional Protections in Utilising Artificial intelligence Systems for Investigating and Inferring Crimes: A Comparative Study   Order a copy of this article
    by Ashraf Fathi Al-Rai, Nayel Musa AlOmran 
    Abstract: This paper explores the potential use of artificial intelligence AI) systems in investigating and deducing crimes and examines their impact on constitutional and legislative guarantees. With the rise of AI entities committing crimes using advanced systems. However, it can be used like any other technical system in the investigation and reasoning process. The problem arises in how to apply constitutional guarantees to the accused when AI systems investigate and deduce the crime. The paper reveals that there are no constitutional or legislative texts in Jordan related to the use of AI systems during the investigation and inference phase of crimes. Although many countries use AI systems for data and information collection, such as the United States of America, Britain, Germany, and the United Arab Emirates, they have not yet used them legally in the inference, investigation, and investigation processes through AI itself.
    Keywords: constitutional; guarantees; artificial intelligence; investigation; reasoning; crimes.
    DOI: 10.1504/IJESDF.2026.10065277
  • Improving reliability with wormhole detection for mobile routing to enhance network security   Order a copy of this article
    by V. Vidya Lakshmi , B. Akash , M. Manesh , A. Praveen , V. G. Rohith  
    Abstract: Problems with mobile routing in lossy, low-power networks (LLNs) are examined in this paper The IPv6 routing protocol for low-power and lossy networks (RPL), which is the IPv6 standard routing protocol for LLNs, has mostly been investigated in static LLNs and does not clearly contain a mobility support mechanism The IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) protocol, which aims to make it possible to transmit IPv6 packets over low-power wireless networks, is one example of an LLN protocol Routing Protocol for Low-Power and Lossy Networks (RPL) and Constrained Application Protocol are other LLN protocols (CoAP) A node in an ad hoc network collects packets from one location and retransmits them to another using a long-distance link inside the network This method determines the wormhole link by computing the largest end-to-end latency between any two nodes in the communication range.
    Keywords: RPL; LLN; hop distance; RSSI; routing protocols.
    DOI: 10.1504/IJESDF.2025.10065316
  • Deep neural network for the purpose of developing an intrusion detection system for wireless sensor networks   Order a copy of this article
    by Swagata Sarkar, B. V. Santhosh Krishna , D. Chithra , Sheshang Degadwala 
    Abstract: A wireless sensor network is composed of a large number of sensor nodes, which collects data and transmits it to a centralised location. They have a lot of security problems, though, because nodes have limited resources. Network intruder monitoring systems do these things for the network, and any information network has to have them. Techniques from the field of machine learning are often used in breach detection systems. Based on the findings of this research, a deep neural network-based intruder detection system was proposed as a solution to this issue and an improvement to performance. Intrusion detection systems can be very helpful in finding and stopping security threats. One way to solve this problem is to use the intrusion detection system with more efficient methods that can choose the best route at every point. The proposed method shows an accuracy of 97.5%.
    Keywords: deep neural networks; DNN; deep learning; DL; wireless sensor networks; WSN; base station; BS; intrusion detection system; IDS.
    DOI: 10.1504/IJESDF.2025.10065369
  • A metaheuristic optimisation-based deep learning model for fake news detection in online social networks   Order a copy of this article
    by Chandrakant Mallick, Sarojananda Mishra, Parimal Kumar Giri, Bijay Kumar Paikaray 
    Abstract: The spread of fake news has become a societal problem. Most often, fake news spreads faster than real news and misleads society. Many works have been proposed in the literature using machine learning techniques to detect fake news, but developing a faster and more efficient model is still a challenging issue. Taking advantage of the deep neural network features of long- and short-term memory (LSTM) and metaheuristic optimisation algorithms, this paper proposes a Salp swarm algorithm-based optimised LSTM model to efficiently classify fake and real news in online social networks. To figure out the superiority of the model, it is experimentally demonstrated that the proposed model outperforms the LSTM optimised with other traditional optimisations. We tested the efficiency of the models on three datasets: the LIAR benchmark dataset, the ISOT dataset, and the news regarding the COVID-19 pandemic, and obtained accuracy of 97.89%, 86.49%, and 99.71%, respectively.
    Keywords: fake news; social network; deep learning; BERT; LSTM; optimisation.
    DOI: 10.1504/IJESDF.2024.10057139
  • Legal regulation of impersonation through websites   Order a copy of this article
    by Abdullah Alkhseilat, Naser Al Ali, Lujain Edweidar 
    Abstract: The worldwide use of the internet has had serious consequences in many areas of life, including its impact on the prevalence of crime, especially crimes against women, of which threats are the most notable. Electronic impersonation of character and personality offences is marked by the perpetrator's intellect, return, power, professionalism, intrusion, and potential natural or moral harm. This crime is based on electronic data and information, requiring identification, creativity, confidentiality, and exclusivity. The increased vulnerability of people's private lives to technology can be associated with the increased storage capacities of both computers and electronic networks, including the internet. These storages contain the most accurate details related to the private lives and electronic private secrets of individuals, including the widespread information available on the internet. Therefore, impersonation and electronic personality crimes are of paramount importance.
    Keywords: cybercrime; Jordanian law; criminal protection; impersonate; website protection.
    DOI: 10.1504/IJESDF.2024.10057782
  • Cryptography in the cloud: securing cloud data with encryption   Order a copy of this article
    by A. Mani, Kiran Shrimant Kakade, P.R. Therasa, M. Vanitha 
    Abstract: Cloud computing utilises dispersed networks to provide computational and storage capacities. It is a kind of efficient technology that is geared specifically for the field of information technology. The use of the Internet has made both accessing data stored in the cloud and recovering that data considerably simpler and more convenient. In a cloud-based system, the storage capacity may be increased by the service providers. In a distributed system, it is generally agreed upon that security is the most important quality to possess. Cryptography is a mechanism that protects data from being seen or accessed by unauthorised parties, such as hackers or snoops. Cloud computing allows its users to store a limitless amount of data and make strategic use of a variety of resources across several dispersed systems. This work offers a privacy-preserving enabled public auditing system and less execution time is required when compared with the other existing methods.
    Keywords: cryptography; public key techniques; public key encryption.
    DOI: 10.1504/IJESDF.2024.10057348
  • Machine learning models for enhancing cyber security   Order a copy of this article
    by P.R. Therasa, M. Shanmuganathan, B.R. Tapas Bapu, N. Sankarram 
    Abstract: Because networks are having an ever-increasing impact on contemporary life, cybersecurity has become an increasingly essential area of research. Virus protection, firewalls, intrusion detection systems, and other related technologies are the primary focus of most cybersecurity strategies. These methods defend networks against assaults from both within and outside the organisation. The ever-increasing complexity of deep learning as well as machine learning-based technologies has been applied in the detection and prevention of possible threats. The objective of this research is to investigate and expand upon the applications of machine learning techniques within the context of the topic of cybersecurity. We offer accessible a multi-layered system that is built on machine learning with the intention of modelling cybersecurity. This will be our key area of focus as we work toward achieving our goal of guiding the application toward data-driven, intelligent decision-making for the aim of protecting systems from being attacked by cybercriminals.
    Keywords: cyberattack; security modelling; intrusion prevention; intelligence on cyber threats; cybersecurity; learning techniques; data science; and determination making.
    DOI: 10.1504/IJESDF.2024.10057194
  • Network security intrusion target detection system in the cloud   Order a copy of this article
    by Durga Prasad Srirangam, Adinarayana Salina, B.R. Tapas Bapu, N. Partheeban 
    Abstract: Cloud computing is a new field that uses the internet to give users on-demand access to a variety of computer resources and services. The framework established in this research project is to maximise the efficiency of security mechanisms deployed in CC settings. Based on a newly invented MH approach known as the reptile search algorithm (RSA), which takes its name from the hunting behaviour of crocodiles, a novel feature selection mechanism has been presented. The RSA improves the performance of the intrusion detection systems (IDSs) framework by picking out just the most important characteristics, or an ideal subset of characteristics, from the functionalities that were recovered by utilising the CNN model. Our study intends to establish a structure for a cloud and fog technology security policy and NSL-KDD dataset is used for the process.
    Keywords: intrusion detection systems; IDSs; assessment; NIDS; suggestions for cloud technology and security; fault diagnosis.
    DOI: 10.1504/IJESDF.2024.10057950
  • Adversarial attack model based on deep neural network interpretability and artificial fish swarm algorithm   Order a copy of this article
    by Yamin Li 
    Abstract: In order to solve the problem of model information leakage caused by the interpretability in deep neural network (DNN), the feasibility of using the Grad-CAM interpretation method to generate admissible samples in the white box environment is proved, and a target-free black box attack algorithm is proposed. The new algorithm first improves the fitness function according to the relation between the interpretation region and the position of disturbed pixel. Then, the artificial fish swarm algorithm is improved to continuously reduce the disturbance value and increase the number of disturbance pixels. The improved artificial fish swarm algorithm uses the strategies of calculating mass and acceleration in gravity search to adjust the visual field and step size of artificial fish, so as to improve the adaptive ability of artificial fish swarm algorithm in the optimisation process. In the experimental part, the average attack success rate of the proposed algorithm in AlexNet, VGG-19, ResNet-50 and SqueezeNet models is 93.91% on average. Compared to the one pixel algorithm, the running time increases by 10%, but the success rate increases by 16.64%. The results show that the artificial fish swarm algorithm based on interpretation method can effectively carry out adversarial attack.
    Keywords: adversarial attack model; deep neural network interpretability; artificial fish swarm; gradient-weighted class activation mapping; Grad-CAM.
    DOI: 10.1504/IJESDF.2024.10057841
  • Comprehensive review of emerging cybersecurity trends and developments
    by Muhammad Ibrar, Shoulin Yin, Hang Li, Shahid Karim, Asif Ali Laghari 
    Abstract: Pakistan views cyberspace as a critical source of power in the twenty-first century when governments no longer have complete control over power games. Private entities, terrorist groups, criminals, and people, on the other hand, are prominent players in cyberspace, offering unpredictable and multifaceted cyber risks to sensitive networks and infrastructure. National security currently necessitates the use of both classic and non-traditional approaches, as well as partnerships between the public and private sectors. Furthermore, the evolving power landscape in cyberspace necessitates the adaptation of theoretical approaches to international relations. Pakistan's increased reliance on cyberspace heightens concerns for global private and government entities' vulnerability to cyberattacks, especially with the surge in wireless communication technology usage. Preventing damage from cyberattacks requires comprehensive measures that include emerging trends, standard security frameworks, and recent developments. As such, this study aims to provide cybersecurity and IT researchers worldwide with an invaluable resource for addressing cyber threats.
    Keywords: cybercrimes; cybersecurity; cyber attacks; emerging trends; challenges.
    DOI: 10.1504/IJESDF.2025.10059222
  • A future prediction for cyber-attacks in the network domain with the visualisation of patterns in cyber-security tickets with machine learning
    by E. Sivajothi, S. Mary Diana, M. Rekha, R. Babitha Lincy, P. Damodharan, J. Jency Rubia 
    Abstract: Support ticket systems have gained popularity as a result of the increase in the use of virtual systems. Since new team members are typically hired during the course of a project, they must be familiar with the features that have already been implemented in the majority of software projects. The goal of this paper is to make clear how using tickets, new team members can be assisted in understanding the features that have been implemented in a project. A novel approach is proposed to categorise tickets using machine learning. The proposed method calculates the number of categories and categorises tickets automatically. While ticket feature visualisation displays the connections between ticket categories and keywords of ticket categories, ticket lifetime visualisation demonstrates time series change to review tickets quickly. Future visualisation designers can overcome comparable difficulties in the field of cyber security by learning about these techniques.
    Keywords: cyber security; cyber-attack; network domains; machine learning.
    DOI: 10.1504/IJESDF.2025.10058296