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 Articles will not be published until the final proofs are validated by their authors.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.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Electronic Security and Digital Forensics (60 papers in press)

Regular Issues

  • Security and Privacy of Adolescents in Social Applications and Networks: Legal Practice of Developing Countries   Order a copy of this article
    by Ahmad Ghandour, Viktor Shestak, Konstantin Sokolovskiy 
    Abstract: The article aims to study the developed countries experience on the legal regulation of cyberbullying among adolescents, to identify existing shortcomings in the developing countries laws, and to develop recommendations for improving the regulatory framework. To do this, the authors have studied the state regulatory practice of the UK, USA, Canada, Malaysia, South Africa and Turkey and analysed the statistics of 2018 on the manifestation of cyberbullying among adolescents in these countries. It turns out that in the countries under review there is either no separate. The percentage of cyber aggression cases among adolescents in developing countries is higher than in developed countries. For example, in South Africa, it is 85%, and in Canada 33%. The results of this study can encourage countries to create separate cyberbullying legislation if they do not have it yet and periodically review and modify already existing legislation.
    Keywords: adolescent protection; cyberbullying; depression; regulations; social networks; suicide.
    DOI: 10.1504/IJESDF.2022.10036942
  • Legal mechanism for regulating responsibilities in the information sphere   Order a copy of this article
    by Aigerim Issakhankyzy, Gulnar A. Alibayeva, Ainur A. Sabitova, Serik K. Zhetpisov, Botakoz S. Shansharbayeva 
    Abstract: The relevance is conditioned upon the fact that the information sphere affects the components of the security of the Republic of Kazakhstan, such as political, economic, social, and others. The purpose of the research is to identify all the characteristic features of the legal mechanism of regulation of the information sphere on the territory of the Republic of Kazakhstan, to consider the functioning of this segment, to identify obstacles and legislative. An important component is the study of the problem of the legal mechanism of regulation in the information sphere on the territory of the Republic of Kazakhstan. In the course of the research, several methodological approaches were used, including theoretical-methodological approach, the method of analysing scientific literature, formal-legal method and others. The results obtained in the course of the study will help to eliminate conflicts in the legal norms, as well as to propose methods of reforming this.
    Keywords: legal regulation of the information sphere; information law; labour law; informatisation; digitalisation; methods of reforming.
    DOI: 10.1504/IJESDF.2024.10052611
    by Ravikumar D, Sasikala K, Vijayashanthi R.S., Narasimha Prasad S 
    Abstract: This article aims to maximise network strong security and its enhancement by presenting different preventative strategies since intrusion detection is essential to computer network security challenges. In this study, intrusion detection is addressed as a challenge of extracting outliers that use the network behaviour dataset, and semi-supervised classification technique based on shared closest neighbours are suggested. Provide a thorough explanation of the fundamentals of cyber security assaults, supervised machine learning methods, and intrusion detection systems. Then, we discuss pertinent initiatives related to the use of supervised methods for intrusion detection. Finally, a taxonomy based on these connected works is offered. This article attempts to offer a sophisticated and distinctive intrusion detection model capable of categorising electrical network events and CDs for smart grids into binary-class, trinary-class, and multiple-class categories. As an effective machine learning model for intrusion detection, it employs the grey wolf algorithm (GWA).
    Keywords: databases; support vector machines; smart grids; cyber attacks; intrusion detection systems; IDS.
    DOI: 10.1504/IJESDF.2024.10052832
  • Security of internet of things based on cryptographic algorithm   Order a copy of this article
    by Sonam Mittal, Soni Singh, BALAKUMARAN D, Hemalatha K 
    Abstract: The desire for automated and connected gadgets has managed to become more significant as the globe continues to advance. The internet of things (IoT), a brand-new idea that focuses around the idea of smart gadgets, has been launched in order to address the situation. The results of this analysis are then used to intelligently govern the operational behaviours of these devices. This study fills this need by describing the design, construction, and practical assessment of a fast deployable internet of things architecture that includes embedded data security. We demonstrate that cryptography that depends on the randomness of wireless link is a great option for the IoT technology. We conclude by discussing the challenges and issues that encryption algorithm is now facing and making recommendations for future research in an effort to make key generation a trustworthy and secure defence against the IoT technology.
    Keywords: safety; internet network; cross-layer security; cryptography; cryptographic algorithms; computer hacking.
    DOI: 10.1504/IJESDF.2024.10052834
  • Image Encryption Based on 3D Arnold and Elementary Cellular Automata Method   Order a copy of this article
    by Rui Yang, Lijuan Feng, Jiangjiang Li 
    Abstract: The traditional image encryption methods have some problems such as poor security and inefficient encryption, this paper proposes a new image encryption method based on 3D Arnold-oriented elementary cellular automata. The new image encryption method first uses 3D Arnold to scramble pixel positions. Then the elementary cellular automata based on quad-tree decomposition is used to further confuse the scrambled images at the specific level to obtain ciphertext images. The experiment results show that this new method can achieve good encryption effect with fewer iteration times and has strong sensitivity to plaintext and key. It also can effectively resist differential attack.
    Keywords: image encryption; elementary cellular automata; 3D Arnold; quad-tree decomposition.
    DOI: 10.1504/IJESDF.2024.10052835
  • Malicious Program Ontology Rule Set Based on Association Decision and Linear Discriminant   Order a copy of this article
    by Chenghua Tang, Min Hu, Mengmeng Yang, Baohua Qiang 
    Abstract: Aiming at the problems of poor scalability and time-consuming in building inference rule sets manually for malware domain ontology, an automatic generation method of malware ontology rule sets is proposed. We extract the behaviour characteristics of malicious programs by defining a formal extended description method based on the frequency of API calls of malicious programs and combining the frequency of API functions. Based on association rules and decision trees, the behaviour characteristics of malicious programs are mined to form a fine-grained redefined rule set of malicious program categories, and SWRL rule language is used to semantic transform the rule set. In addition, the coarse granularity classification of program behaviour rules is implemented based on Fisher linear discriminant algorithm. The generation efficiency of malware ontology rules generated by us is 10.08 pieces/second, and the inference detection rate of unknown samples reaches 89.92%.
    Keywords: malicious programs; behaviour ontology; SWRL rule set; API functions; behaviour characteristics.
    DOI: 10.1504/IJESDF.2024.10052884
  • Data Hiding using Video Steganography   Order a copy of this article
    by Ravichandran C, Ashok Vajravelu, Sankarsan Panda, Sheshang Degadwala 
    Abstract: Video steganography aims to hide the presence of a communication from a hostile third party. One of the techniques recommended in this study is the hash-based least significant bit method for video steganography. The study conducts an in-depth analysis of the numerous enhancements that have been made to the safety of data transmission, as well as the several methods that have been adapted in order to accomplish the same goal. The results of the MATLAB simulation show that the proposed method is superior to other state-of-the-art methods that are currently in use. According to the findings of the comparison, the data-hiding method that has been proposed provides increased safety and reduces distortions for improved video quality. The results of our experiments suggest that our algorithm offers a high level of protection while having just a minimal effect on video quality.
    Keywords: cover video; steganography; LSB technique; watermarking; AES; peak signal-to-noise; intra-prediction mode; integer wavelet transform; temporal correlation.
    DOI: 10.1504/IJESDF.2024.10052934
  • Formulation of a two-level electronic security and protection system for malls   Order a copy of this article
    by Thirumurugan Thirugnanam, Leena Bojaraj, Lavanya R, Nagalakshmi T.J 
    Abstract: Electronics are everywhere around us these days, and many of them help us maintain security in different locations. However, there are still numerous security issues that banks, residences, and other establishments must deal with. The real-time identification of possibly suspicious actions in shopping malls is the main goal of the comprehensive expert system we present in this article. Our video surveillance technique makes a number of creative suggestions that combine to create a solid application that effectively tracks people’s movements and identifies suspicious activity in a retail setting. The discussion of several present and developing solutions aimed at obtaining a high level of trust in IoT applications follows the discussion of security concerns. Four potential technologies blockchain, edge devices, cloud technologies, and machine learning are examined. An experiment demonstrates that in the same dependable network environment of DCNs, our responsibility security routing system performs better.
    Keywords: electrical gadgets; video monitoring in malls; background removal; RFID tags; barcode reader.
    DOI: 10.1504/IJESDF.2024.10053197
  • An original data encryption technique for communication networks
    by A. Rajasekar, KARUNAKARAN A, Sivakumaran C, Sheshang Dipakkumar Degadwala 
    Abstract: A novel secure distribution technique of network communication data is developed based on data encryption algorithm to address the issues of poor transfer effectiveness and high transmission bit error rate in previous transmission methods. In order to design the cipher text protocol, the access to network communication data is controlled by the agentless key publishing protocol. According to the experimental simulation findings in this work, the SM2 method (Supermemo2) can produce a 256-bit key very rapidly. The research’s findings indicate that using link cryptographic algorithms in network communication security can increase security by 25%. The original deep learning chaotic encryption algorithm’s performance flaw is optimised in this research. For wireless communication security, a chaotic neural network approach with dynamic keys is suggested. The experimental findings demonstrate that the technique suggested in this study significantly improves the speed of encryption and decryption as well as the key’s capacity to resist decoding.
    Keywords: neural network; key cryptographic technique; expected release terminal; communication systems; bilinear map-based method; key creation; encryption/decryption algorithms.
    DOI: 10.1504/IJESDF.2024.10059345
    by Alagappan Annamalai, Ramesh Chandra Poonia, Suresh Shanmugasundaram 
    Abstract: A virtual private social network (VPSN) allows a device to communicate securely with a network via the internet. Confidential data may be sent more securely thanks to the encrypted connection; it allows the user to operate remotely and prevents unauthorised parties from listening in. A mobile messaging platform is a text-enabled mailbox on the web. They enable companies and organisations to communicate with clients via text message. Social networking organisations may now use compute resources as a utility instead of creating and managing their computer infrastructures thanks to cloud computing (CC). An example of how a distributed sensor-actor environment might be used in a sociology-technical network is shown in this paper. The results are obtained as regular media access actively is 85.7%, security services in IoT is 85.37%, text attackers is 83.6%, loss of information is 82.8%, and blocking text messages is 91.48%.
    Keywords: internet of things; IoT; cloud computing; virtual private; social; networks; mobile platform.

  • Using DNA to Develop a Lightweight Symmetric Encryption Method to Encrypt the Data of IoT Devices
    by Bassam Al-Shargabi, Rame Jamil Al-Dwairi, Mohammed Abbas Fadhil Al-Husainy 
    Abstract: The security and integrity of the data generated from the internet of things (IoT) devices transmitted via networks must be preserved. Traditional encryption methods are used to encrypt IoT data, but they require more processing power, which IoT devices lack (CPU, memory, storage). In this research, we proposed a DNA-based lightweight symmetric encryption (DLSE) method with simple operations and flexible multi-encryption rounds to be deployed to various IoT devices. The encryption key of the DLSE method is derived from a DNA random sequence to produce a unique key for each round, making it more difficult for attackers to break. The experiments show that the DLSE method has outstanding performance compared to AES and 3DES, with the best encryption time and the best proportion of distortion at the highest level of security. Moreover, the DLSE method proved efficient and can be adapted to meet IoT devices’ computational resources.
    Keywords: lightweight encryption; data encryption; security; multi-round encryption; internet of things; IoT; cyber security; DNA-based lightweight symmetric encryption; DLSE.

  • Detection of Botnet using Deep Learning Algorithm: Application of Machine Learning in Cyber-Security   Order a copy of this article
    by Siva Kumar A, Jency Rubia J, Hima Vijayan, Sivakumaran C 
    Abstract: Machine learning has been made possible as a result of the availability and accessibility of a massive amount of data gathered by internet-connected sensors. The concept of machine learning exhibits and spreads the notion that a computer has the potential to develop itself over the course of time. We investigate a variety of security applications from a variety of angles in which ML models play a key role, and we compare the accuracy outcomes of these models using a variety of conceivable dimensions. To provide an accurate depiction of the qualities associated with security, we have shown the threat model and defence strategies against adversarial attack techniques. The proposed method shows about 88% accuracy for the used data. These attacks are based on the fact that the adversaries are aware of the model.
    Keywords: adversarial attack; security; machine learning; deep learning; LSTM.
    DOI: 10.1504/IJESDF.2024.10053550
  • A Retrospective Analysis on Fully Homomorphic Encryption Scheme   Order a copy of this article
    by Sonam Mittal, Dr.K.R.Ramkumar Kumar 
    Abstract: Many researchers of cybersecurity have started a hunt for refining the data encryption models for real-life applications. The security and confidentiality of data over cloud as a third party is a big issue, to the users. To overcome this problem, cloud uses some encryption methods for data security. The FHE, an unconventional technology provides unrivalled capabilities to perform computation on encrypted text to facilitate the secure computation for big-data analysis. The paper presents a review of existing standards of FHE schemes such as lattice-based, integer-based, LWE, RLWE, and NTRU. Various challenges need to be addressed are listed, to model the more competent, effective, and dynamic FHE model. The concepts that underpin these schemes are discussed, and their performance and security concerns. The paper helps to understand the different hurdles that need to be overcome for real-life applications and help to find the direction for their research for better FHE scheme.
    Keywords: fully homomorphic encryption; Gentry; DGHV; learning with errors; LWE; ring learning with errors; RLWE; Nth degree truncated polynomial ring units; NTRU.
    DOI: 10.1504/IJESDF.2024.10053552
  • A Comparison Study to Analyze the Data Acquisitions of iOS and Android Smartphones Using Multiple Forensic tools   Order a copy of this article
    by Faleh Alshameri, Katrina Khanta, Stephen Boyce 
    Abstract: Nowadays, most people carry their smartphones with them wherever they go, and it has become one of the primary necessities to not leave your home without. Because of the ever-growing rise in smartphone usage, we store most of our personal information into these handheld devices as they have evolved into an extension of ourselves. Additionally, it is critical to acknowledge how multiple mobile applications are connected to cloud storage. The purpose of this study is to emphasise the significance of data remanence located within mobile devices and how forensic data acquisitions from smartphones prove as high-value evidence in legal cases. We conducted an experimental methodology using various data acquisition tools, such as Magnet AXIOM, oxygen forensic detective, Belkasoft X. and MSAB XRY, to extract data from previously used smartphone devices purchased from an eCommerce website. The study provides a comparative analysis between the data acquisition tools and the following smartphones: Apple iPhone 8, Samsung Galaxy S9+, and Google Pixel 3, to determine which tools are more effective at extracting a specific range of deleted datasets.
    Keywords: smartphone forensics; mobile forensics; internet of things; IoT; forensic data acquisition; digital evidence recovery; android; Apple; iOS.
    DOI: 10.1504/IJESDF.2024.10053589
  • Dark web data classification using Deep neural network   Order a copy of this article
    by Sathish Kumar P.J, Jency Rubia J, ANITHA R, Sheshang Degadwala 
    Abstract: The dark web is an overlay network comprised of the darknet, which can only be accessed via specialised software and a predetermined permission scheme. This article investigates the development of dark web intelligence as a means of enhancing cybercrime prevention tactics in several countries. On the basis of machine learning, we develop, analyse, and assess the effectiveness of darknet traffic detection systems (DTDS) in IoT networks. We focussed at the safety features that are available to users, as well as their motivations and the ability to revoke their anonymity. In addition, we perform a depth analysis by automating the process of detecting hostile intent from the darknet. Finally, we compared our proposed system to various already existing DTDS models and showed that our best results are an improvement of between 1.9% and 27% over the models that were previously considered to be state-of-the-art.
    Keywords: darknet; traffic analysis; network management; deep learning neural networks; real-time forensics; darknet traffic detection systems; DTDS.
    DOI: 10.1504/IJESDF.2024.10053710
  • Deep Learning-Based Image Forgery Detection System   Order a copy of this article
    by Helina Rajini Suresh, Shanmuganathan M, Senthil Kumar, VIDHYASAGAR BS 
    Abstract: Despite the fact that there are more complex ways of forgery being developed all the time, image forgery detection continues to play an essential part in the field of digital forensics. The problem of counterfeit photographs is today a worldwide problem that is mostly distributed via social networking sites. The ability to identify phoney pictures eliminates the possibility that fraudulent photographs may be used to trick or damage other people. Within the scope of this research, we investigate the deep learning technique to image forgery detection. The proposed model implemented by python language uses input images in batches and a convolutional neural network (CNN) using ResNet50v2 architecture and YOLO weights. We analysed the CASIA v1 and CASIA v2 benchmark datasets. For the purposes of training, we used 80% of the data, and the remaining 20%t was used for testing. 85% accuracy obtained for the dataset.
    Keywords: machine learning; deep learning; image forgery; ResNet50; YOLO; CNN.
    DOI: 10.1504/IJESDF.2024.10053856
  • Network Intrusion Detection: Systematic Evaluation Using Deep Learning   Order a copy of this article
    by Kiran Shrimant Kakade, Nagalakshmi T.J, Pradeep S, Tapas Bapu B. R 
    Abstract: Hackers have always regarded getting information on the health of computer networks to be one of the most significant aspects that they need consider. This may include breaking into databases as well as computer networks that are utilised in defensive systems. As a result, these networks are constantly vulnerable to potentially harmful assaults. This paper provides an assessment technique that is based on a collection of tests, with the goal of measuring the effectiveness of the individual elements of an IDS as well as the influence those components have on the whole system. It evaluates the deep neural network’s potential efficacy as a classification for the many kinds of intrusion assaults that may be carried out. Based on the results of the studies, it seems that the level of accuracy achieved by intrusion detection using deep convolutional neural network is satisfactory.
    Keywords: machine-learning; networks intrusion detection systems; and networks.
    DOI: 10.1504/IJESDF.2024.10054079
  • A Novel Color Image Encryption Algorithm using S-box Technique   Order a copy of this article
    by Kiran Shrimant Kakade, Swagata Sarkar, Asha S, Sivakumaran C 
    Abstract: The combined 3D image suggests that SHA-256 is responsible for seeding the memristive chaotic system with initial values. The suggested picture encryption method uses the encrypted image's output value to set the algorithm's parameters. Second, either discrete Arnold map and indeed the quantum chaotic map are used to construct the structure of permutations and gray-level encryption, respectively. A classical chaos sequence modifies the pixel value before it is permuted using the Arnold transform. We use the S-box to introduce non-linearity and diffusion to image files, and then we use the Boolean function XOR to the encrypted picture to provide even more randomness. Additionally, we examine randomness tests such as NIST-R, correlation, and key evaluation. The efficiency of the proposed method is evaluated in relation to many similar existing algorithms. Both theoretical and practical studies support the reliability of our methodology.
    Keywords: Chaotic image encryption; cryptanalysis; RNA mutation; 3D Arnold matrix; Logistic Map; S-box; Cryptanalysis.
    DOI: 10.1504/IJESDF.2024.10054099
  • A comparative study of Covert Channel attacks in Android with different parameters and detection tools   Order a copy of this article
    by Abhinav Shah, Dr. Digvijaysinh Rathod, Yash Mehta 
    Abstract: With the evolving technology worldwide, there has been an increase in technology usage. The internet has brought a considerable revolution in information technology, and cyber security is one of the fields growing at a tremendous speed daily. With the invention of new technologies such as artificial intelligence, blockchain, and machine learning, security researchers and experts will focus more on using these new technologies. Smartphones are now widely used for communication, social media, and multimedia. Here, the author has tried to focus on studying covert channel attacks through android applications and devices. The evolution of Android applications has been more than a decade, and it is continuing. In the past, attackers have found a way to create a covert channel to pass some sensitive information through android applications/devices. This paper has provided a comparative study of the different parameters used to create covert channel attacks and analysed various tools to detect such attacks. The author has provided a new approach to creating a covert channel attack in Android applications and showcased the theory along with the experiments and results.
    Keywords: Android application; covert channel; cyber security.
    DOI: 10.1504/IJESDF.2024.10054215
  • Infrared and visible image fusion based on improved NSCT and NSST   Order a copy of this article
    by Shahid Karim, Geng Tong, Muhammad Shakir, Asif Ali Laghari, Syed Wajid Ali Shah 
    Abstract: Image fusion has several critical applications, which reveals its importance. The substantial information from infrared and visible images can be combined with effective image fusion. There is ample destroyable directional information that cannot be leveraged by several image fusion methods, specifically, the images taken from a long distance, with low resolution, in the dark, during bad weather, and many more. To address these problems, we have improved the non-subsampled contourlet transform (NSCT) and non-subsampled shearlet transform (NSST) by adaptive pre-processing. This paper also describes the basic structure, working principle, limitations, and applications of NSCT and NSST. Furthermore, we have presented multiple fusion results and explained the differences. Finally, we have discussed the pros and cons and a few recommendations for improving NSCT and NSST.
    Keywords: wavelet; contourlet; shearlet; image fusion; multimodal.
    DOI: 10.1504/IJESDF.2024.10054426
  • Functional encryption implementation to protect storage data in the cloud   Order a copy of this article
    by Sulakshana Mane, Kiran S. Kakade, Shibu S, Suvitha S 
    Abstract: Large volumes of electronic data have been created in recent years, and enterprises that require data recovery services may be affected by a variety of natural or man-made disasters, resulting in massive data losses. Encryption is the most reliable solutions for enhancing security and privacy. Any security vulnerabilities have been uncovered, such as data loss while data is stored in cloud storage. To safeguard data transactions in the public cloud, this study presents a layered encryption approach. In the suggested method paradigm, asymmetric and symmetric cryptography are mixed. Encryption primitives such as M ulti-Client Functional Encryption (M CFE) enable an evaluator without needing to obtain each client's plaintext set to discover the intersection of all sets. An extensible variant of the M CFE techniques for set intersection is presented in this study. According to our analysis, this relaxation is required for the practical application of secure multi-client set intersections.
    Keywords: Functional encryption; cloud computing; cryptography; cyberspace; cloud storage; IT infrastructure; decryption; cryptographic primitive.
    DOI: 10.1504/IJESDF.2024.10054471
  • Security challenges for routing protocols in mobile ad hoc network   Order a copy of this article
    by Kiran Shrimant Kakade, Rajesh C, Veena T, Sivakumar P 
    Abstract: Mobile ad hoc networking (MANET), is a set of wireless networks that may automatically configure itself and have more than one hop. Due to the traits it has, MANET is more susceptible to a variety of various kinds of assaults and security concerns. In this research, the cuckoo search algorithm, which discovers the best hops in advancing the routing, using an algorithm for confidence protected as well as energy-efficient navigating in MANETs is used to provide a trust-based safeguarded as well as energy-efficient navigational in MANETs. This is accomplished by employing a trust-based protected as well as energy-efficient navigating in MANETs. Even in the absence of an adversary, the secure optimisation routing method that was suggested achieved the following results: a minimum energy consumption of 0.10 m joules; a minimal delay of 0.0035 m sec; a traffic load of 0.70 bps and 83% detection accuracy.
    Keywords: MANET; routing protocols; security challenges; fuzzy clustering; cuckoo search.
    DOI: 10.1504/IJESDF.2024.10055130
  • Forensic examination of digitally captured handwriting- A review of contemporary tools and techniques   Order a copy of this article
    by PRIYA SHARMA, Om Prakash Jasuja 
    Abstract: The digital revolution is causing a paradigm shift in writing tools. Handwriting can be digitally captured using a variety of tools with different features. A forensic handwriting examiner (FHE) may come across such kind of handwriting for examination. Digitally captured handwriting (DCH) provides qualitative as well as quantitative information about the dynamics of the handwriting process. Several frameworks and methods are suggested for analysing digitally captured handwriting and signatures. This review has explored the most recent advancements and challenges in the forensic analysis of dynamic handwriting and signatures. The majority of DCH research focuses on automatic handwriting verification or recognition through algorithms, but none of it considers a forensic perspective. Additionally, the generalised framework for forensic analysis of digitally captured handwriting or signatures is inadequate, and a standard framework must be established.
    Keywords: forensic examination; handwriting; signature; dynamic signatures; forensic handwriting examiner; FHE.
    DOI: 10.1504/IJESDF.2024.10055680
  • Web Browser Forensics: Mozilla Firefox   Order a copy of this article
    by Hitesh Sanghvi, Dr. Digvijaysinh Rathod, Parag Shukla, Ramya Shah, Yashrajsinh Zala 
    Abstract: Aside from Internet Explorer, Mozilla’s Firefox is one of the most established and popular web browsers. These days, Firefox allows private (Incognito) windows just like every other browser. According to Mozilla Firefox, private browsing does not save cookies or browsing history and leaves no traces behind when sessions are ended. If offenders commit crimes while using private browsing, this will present a barrier to digital forensic analysis because there would not be any computer-based evidence. While Mozilla Firefox was open in both normal and private modes, we conducted an in-depth investigation to assess the status of the evidence. In the experiment, activities were carried out in one virtual machine in regular mode and another in a private Firefox window. Later, we carried out forensic acquisition of the RAM and Hard drive and evaluated what kinds of evidence were discovered from both VMs. The tools FTK and Autopsy were used to analyse the collected data. We found the important evidence of various activities obtained related to Google, YouTube, Twitter, Amazon, Facebook, Outlook, Yahoo and Gmail using FTK and Autopsy through the hard disk and RAM forensics while Firefox opened in normal and private mode. We can also determine from this result that FTK carves more data from the image file than autopsy.
    Keywords: Mozilla Firefox; FTK; autopsy; RAM forensics; HDD forensics; private browsing; digital forensics.
    DOI: 10.1504/IJESDF.2024.10055704
  • A review of research in forensic investigation of cryptocurrencies   Order a copy of this article
    by Borase Bhushan Gulabrao, Dr. Digvijaysinh Rathod 
    Abstract: In last one decade, use of cryptocurrencies in various fields has increased phenomenally. It offers many benefits to the users. It also has emerged as one of the major challenges for law enforcement agencies across the world. Research has been conducted to identify forensic artefacts for various cryptocurrencies used in different wallets and on different platforms. This paper aims to analyse and sum up the existing literature on forensic investigation of cryptocurrencies. This review paper makes mention of forensic investigation of six different cryptocurrencies, 30 different types of wallets and of 49 different types of forensic artefacts. It also mentions 25 different tools used in forensic investigation. Paper briefs about seven different cryptocurrency visualisation and analysis tools. Finally, the paper highlights about research gaps in this field.
    Keywords: blockchain; cryptocurrency forensics; forensic artefacts; wallets; Bitcoin; Monero; Verge; Litecoin; Dogecoin; memory forensics.
    DOI: 10.1504/IJESDF.2024.10055706
  • The Application of AI Techniques for Firearm detection in Digital Forensic Investigation   Order a copy of this article
    by Suraj Harsha Kamtam, Harjinder Lallie, Muhammad Ajmal Azad 
    Abstract: The early detection of potential violent situations involving firearms is a useful aid to law enforcement. AI and automation can complement humans in weapon detection as it excels in repetitive tasks and make clear judgments of ambiguous situations. AI technology can be used in digital forensic investigations to detect objects such as firearms and predict features such as age, and gender. This paper demonstrates the application of a model called you only look once (YOLOv3), a deep neural network, which was used to build a custom firearm detection model. The proposed model can automate the repetitive, tedious and error-prone task of searching through a large number of images for the presence of firearms, thus reducing human effort and stress. Five models have been trained in this paper on different scenarios to understand the performance of YOLOv3 which include one firearm, multiple firearms (pistol and rifle), greyscale images, factual scene and L-shape false positives. Our model achieved a maximum mean average precision of 97.68% and a minimum of 59.41%. The models developed in this work outperforms existing models which do not scale well and cannot detect changes in image, noise, shape and background.
    Keywords: digital forensics; video forensics; you only look once; YOLO; YOLOv3; firearm detection; transfer learning; computer vision; convolutional neural network.
    DOI: 10.1504/IJESDF.2024.10055847
  • Essence, concept and types of national legislation in the field of information technology   Order a copy of this article
    by Sergey Manin, Salahiden Sabikenov, Yelena Manina 
    Abstract: The relevance of scientific work is due to the high level of development of innovative technologies in the world community, which contributes to the development of new approaches to ensuring the interests of citizens, including in the information segment. The target areas of scientific research are the disclosure of national legislation in the field of information technology in Kazakhstan. The implementation of the task set is possible due to the use of dialectical-methodological and comparative methodological approaches, system analysis, synthesis method, and others. The results of the study showed that at this stage of the formation of legislation in the field of information technology, it develops in accordance with global trends in the field of regulatory regulation of information technology and creates conditions for the liberalisation of the information and communication technology market. Some aspects of the information legislation requiring reform and improvement were also highlighted.
    Keywords: information society; information and communications technologies; e-government; digital economy; information security.
    DOI: 10.1504/IJESDF.2024.10056202
  • Deep Learning for Smart Home Security Systems : CNN based Home security   Order a copy of this article
    by Balasubramanian M, Kiran S. Kakade, Sulakshana Mane, Sujatha Jamuna Anand 
    Abstract: A smart home enables new modes of connection and the consumption of various services. Additionally, AI and deep learning have aided in the enhancement of many services and jobs by making them more automated. In this study, we used IoT and deep learning to create a safe and efficient home automation system. Using deep learning approach, the user is able to operate appliances such as fans, televisions, bulbs, and other electronic or electrical equipment by either speaking commands into their mobile device or using an application that is pre-installed on their mobile device. The results of the trials that were carried out demonstrate that the suggested deep learning model is more accurate than the KNN method, and that the RL system improves the user’s quality of experience by as much as 3.8 points on a scale of 10.
    Keywords: deep learning; smart home; IoT; RL.
    DOI: 10.1504/IJESDF.2024.10056371
  • Cloud Computing-Based Computer Forensics: A Deep Learning Technique   Order a copy of this article
    by Kalaiarasi D, Leopauline S, KRISHNAVENI S., Ashok Vajravelu 
    Abstract: Forensics on the cloud is an addition of forensic research that safeguards against cybercriminals. The development of technologies that use the cloud to store, retrieve, and archive data has resulted in changes to these processes. Our method for monitoring users’ information makes use of graph theory-based deep neural networks embedded in smart contracts (GNNSC). Finally, an evidence graph that is built on a blockchain makes it possible to analyse evidence. It is suggested that this Scheme be used to get the most accurate predictions during the process of looking into log data and separating it into normal and strange entries. The VLCS method is used to find the best solution. It is based on a modified cuckoo search algorithm with a variational parameter and a logistic map. The cloud forensics solution methodologies needed to conduct effective investigations are included in the suggested taxonomy.
    Keywords: cloud computing; edge computing; computer forensics; forensic software tools; digital forensic.
    DOI: 10.1504/IJESDF.2024.10056496
  • An effective Network Security Scrutinizing Method Based on Deep Learning   Order a copy of this article
    by Sivakumar K, Rajesh C, Julia Faith S, Narasimha Prasad S 
    Abstract: The field of network security is constantly evolving. Future dangers are difficult to predict and even more challenging to prepare for. In order to effectively confront future network security concerns, this article discusses efforts made to construct a vital support capability for an autonomous network security testing system. The purpose of this system is to simulate future network attacks on vital infrastructure in order to better protect against them. A novel attack paradigm is proposed, one that allows for more awareness and control inside a network of compromised nodes. The suggested attack framework has low memory and network requirements while still allowing for the retrieval and execution of arbitrary attacks. This framework makes it easier to conduct rapid, autonomous penetration tests and assess the state of detection systems and procedures ahead of time for autonomous network-attacks.
    Keywords: network security; cybersecurity; deep learning; artificial intelligence.
    DOI: 10.1504/IJESDF.2024.10056596
  • A Framework for Security of Public Cloud Environment   Order a copy of this article
    by Manju Lata, Vikas Kumar 
    Abstract: Majority of organisations tend to subcontract the computational requirements toward public cloud services to minimise the capital expenses. However, the desire of minimum cost and higher efficiency also acquire the data risk and misuse in a number of cases. Correspondingly, the security concerns keep on growing for the public cloud deployments. These include the challenges related to core security, trust, compliances as well as service management etc. Different aspects of the public cloud security have been presented in this work with specific examples and associated implications. Both the technology perspective and the service perspective have been taken care of to illustrate the need for a comprehensive security framework. Relevant security parameters have been identified and a comprehensive cloud security framework has been presented to take care of the security needs of public cloud deployments. The framework offers the essential parameters and techniques that should be taken-up in the design of public cloud deployments. The implementation of the framework will go a long a long way in offering security to the public cloud frameworks.
    Keywords: public cloud services; public cloud security; security parameters; tools and techniques; public cloud security framework; public cloud environment.
    DOI: 10.1504/IJESDF.2024.10056794
  • SQL Injection Authentication Security Threat   Order a copy of this article
    by Sulakshana B. Mane, Kiran Shrimant Kakade, Shyamala Prakash Shingare, Nanasaheb Halgare 
    Abstract: This document represents the one of the application security risk SQL poising which effect on the database data which is the heart of server. While a person who is taking advantage of this vulnerability is able to infuse a SQL inquiry as well as influence its SQL information via utilising a method that allows them to escape character or be an invalid person includes injecting (also known as 'infusing') a SQL queries into the information flowing between the clients towards the applications. An effective SQL infusion exploits can interpret delicate information from the data set, change the data set information (supplement/update/erase), executes organisational procedure on the data set (like closing the DBMS), restore the information of such a specified document that is available mostly on DBMS record framework, and even at times concern instructions to such operating system.
    Keywords: SQL; web application; vulnerabilities; attack.
    DOI: 10.1504/IJESDF.2024.10056795
  • A Meta Heuristic Optimization based Deep Learning Model for Fake News Detection in Online Social Networks   Order a copy of this article
    by Chandrakant Mallick, Sarojananda Mishra, Parimal Giri, Bijay 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
    by Therasa P.R., Shanmuganathan M, Tapas Bapu B. R, SANKARRAM N 
    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
  • Cryptography in the Cloud: Securing Cloud Data with Encryption   Order a copy of this article
    by Mani A, Kiran Shrimant Kakade, Therasa P.R., Vanitha M 
    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
  • Security in database management system using machine learning   Order a copy of this article
    by Deepa M, Dhilipan J 
    Abstract: The term 'database security' refers to the collection of rules, tools, and processes that have been developed to maintain and protect the databases’ confidentiality, integrity, and accessibility. The use of machine learning to improve database management security is becoming more common. The fundamental goal of employing machine learning in security is to make the process of malware detection more actionable, scalable, and successful than conventional techniques, which need the participation of humans. This may be accomplished by making the process more automated. The process entails overcoming problems posed by machine learning, which need to be managed in an effective, logical, and theoretical manner. Machine learning algorithm is applied in the critical paths of the tuner. The optimum configuration for the proposed system yields a throughput boost of between 22% and 35% and a latency reduction of around 60%. The method is robust to various attacks.
    Keywords: database security'; security techniques; database threats; integrity; machine learning.
    DOI: 10.1504/IJESDF.2024.10057609
  • 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, particularly crimes against women, most notably the threat. Electronic impersonation of character and personality offences are marked by the perpetrators intellect, return, power, professionalism, intrusion, and potential natural or morality. This crime is based on electronic data and information, and it requires identification, creativity, confidentiality, and exclusivity. Given the increasing vulnerability of peoples private lives to it technology, associated with increased storage capacities of both computers and electronic networks, including the internet, and their containing the most accurate details related to the private life and electronic private secret of individuals, and the widespread information available on the internet, impersonation and electronic personality crimes are of paramount importance.
    Keywords: Cybercrime; Jordanian Law; Criminal Protection; Impersonate; Website Protection.
    DOI: 10.1504/IJESDF.2024.10057782
  • 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 Grad-CAM interpretation method to generate admissible samples in 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
  • Network security intrusion target detection system in the cloud   Order a copy of this article
    by Durga Prasad Srirangam, Adinarayana Salina, Tapas Bapu B. R, PARTHEEBAN N. 
    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
  • 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
  • Predictive Modeling for Fake News Detection Using TF-IDF & Count Vectorizers   Order a copy of this article
    by Divya Singhal, Richa Vijay 
    Abstract: Most people choose to acquire their news quickly and affordably via the internet, yet this encourages the fast spread of false information. Today’s society depends heavily on data, and by 2023, 120 zeta bytes will be released every second. This enormous amount of data is transforming the world thanks to several technologies. People rely on online news sources to stay current on events as the Internet has grown in popularity. The growth of social media sites like Instagram, YouTube and Facebook, information spread quickly to people all over the world in a short amount of time. Fake news might also proliferate because of this, which would have an impact on both society and people. Fake news must be discovered and eradicated before it further harms the country. Because of how false news functions, it may be hard to spot. In this paper, we provide a paradigm for recognising fake news. The research is conducted on Python using Scikit-Learn and NLP-util library. The research examines on detecting fake news and investigating traditional machine learning models to determine the best approach. The data was utilised to train seven classifiers using the TF-IDF and count vectoriser, and the results to select the best suited features to get the greatest accuracy and F1-score are shown using confusion matrix.
    Keywords: fake news detection; predictive analysis; supervised learning; natural language processing; TF-IDF vector; count-vector; machine learning.
    DOI: 10.1504/IJESDF.2024.10058060
    by Sivajothi E, Mary Diana S, Rekha M., Babitha Lincy R, Damodharan Palaniappan, Jency Rubia J 
    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
  • 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
  • Comprehensive Review of Emerging Cyber security 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 cyber-attacks, especially with the surge in wireless communication technology usage. Preventing damage from cyber-attacks requires comprehensive measures that include emerging trends, standard security frameworks, and recent developments. As such, this study aims to provide cyber security and IT researchers worldwide with an invaluable resource for addressing cyber threats.
    Keywords: Cyber Crimes; Cyber Security; Cyber Attacks; Emerging Trends; Challenges.
    DOI: 10.1504/IJESDF.2025.10059222
  • Right of Attribution in Digital Children's Literature   Order a copy of this article
    by Asem Baniamer, Noor Issa Al-Hendi 
    Abstract: This study addresses the right of attribution in digital children’s literature, highlighting the intertwining that exists between the rigidity of the right of attribution in the current copyright legislations and laws, and the rapid technological developments that occurred in digital children’s literature. The study focuses on the term ‘digital children’s literature’, its concept, nature, the new technical elements that emerge because of the rapid technological developments, and the legal complexities that resulted from such developments but were not regulated by the traditional author’s rights laws. The study also identifies some areas of legislative deficiency and legal gaps in the life cycle that regulates intellectual property. The study aims to discuss the term and concept of digital children’s literature and clarify the nature of the technical developments that affected this type of literature.
    Keywords: children’s literature; right of attribution; digital literature; computation; interconnection; interactivity.
    DOI: 10.1504/IJESDF.2024.10059402
  • Human rights information in the context of digitalisation   Order a copy of this article
    by Narkes Zhexembayeva, Arailym K. Jangabulova, Guldana A. Kuanalieva, Makhabbat K. Nakisheva, Bahytkul M. Konysbai 
    Abstract: The relevance of the research resides in digitalisation and information technologies introduced into everyday life. For example, a new cluster of so-called digital rights appears during digitalisation, which is not sufficiently developed today. These include the human right to access the internet, the right to protect the user from unwanted information, and so on. At the same time, in the legislative systems of several states, there are already regulatory legal acts that are aimed at the legal regulation of information. The purpose of this study is a comparative analysis of ways to protect human rights in the context of the introduction of information technologies. Within the framework of the research, along with several general scientific methods, special methods are also used. In particular, they include methods of historical analysis, induction, and deduction. The information presented in this study can be used by public authorities.
    Keywords: personal data; network security; Kazakhstan; international law; internet; legal regulation.
    DOI: 10.1504/IJESDF.2023.10052382
  • Denim enumeration and tabulation solution for the garments manufacturing environment   Order a copy of this article
    by Muhammad Shakir, Shahid Karim, Shahnawaz Ali, Shahzor Memon, Halar Mustafa, Rabia Shaikh 
    Abstract: As the denim industry production is growing every day and the establishment of new industries is developing rapidly, the problem that is being faced by the industry is greater time consumption regarding counting the denim product. The main objective of this study is to help denim industries use their time effectively. The vision is to modernise the old-school method of finished denim production. The desktop application will bridge the gap between the physical and digital worlds, linking the enumeration technique with desktop applications. An incredibly convenient way of keeping a record of produced denim. Denim production is the root of the garment industry's earnings and by digitalising the enumeration technique, we can make it error-free and speedy. It counts every denim piece and will also scan the denim for metal pieces. The desktop application of this project is built using Microsoft Visual Studio.
    Keywords: denim counter; denim enumeration; tabulation; metal-free denim.
    DOI: 10.1504/IJESDF.2024.10052813
  • Legal awareness and its significance when determining the nature of a person's legal behaviour   Order a copy of this article
    by Ermek Abdrasulov, Akmaral Saktaganova, Indira Saktaganova, Sayash Zhenissov, Zhassulan Toleuov 
    Abstract: Legal awareness is considered a part of consciousness and is an important component of social adaptation. That is why knowledge is especially significant in how legal awareness affects the nature of human behaviour. The purpose of this article is to investigate possible behavioural features of legal behaviour and their relationship with legal awareness, as well as to understand how legal awareness and its qualitative characteristics affect the nature of legal behaviour. Classical methods of information analysis and structuring, methods of classification, logical explanation, finding causal relationships, and generalisation were used as research methods. Because of the research, the main provisions of the relationship between the qualities of legal consciousness and the nature of the legal behaviour of the individual were formed. A conclusion was made about the influence and the importance of the qualities of legal awareness on the nature of the legal behaviour of the individual.
    Keywords: non-conformal legal consciousness; conformal legal consciousness; legal consciousness and religion; social adaptation; defects of legal consciousness.
    DOI: 10.1504/IJESDF.2023.10052307
  • Google chrome forensics   Order a copy of this article
    by Hitesh Sanghvi, Digvijaysinh Rathod, Salem Yahya Altaleedi, Abdulaziz Saleh AlThani, Mohammed Abd Alrhman Alkhawaldeh, Abdulrazaq Almorjan, Ramya Shah, Tanveer Zia 
    Abstract: Google Chrome is used to explore the internet and navigate websites. Users prefer incognito mode because it claims that it does not keep crucial information in the computer, ensuring privacy and security of browsing data. While offenders employ incognito mode browsing to perpetrate a crime, digital forensics investigators face new technical obstacles in recovering evidence. We have presented the evidence obtained in Google Chrome while it is open in normal and incognito mode. We performed 78 activities and hard drive and RAM forensics were performed using FTK and autopsy. We unearthed artefacts in the cases of deleted bookmarks and history, Gmail and Yahoo mail, Facebook chat, and web WhatsApp chat while Google Chrome is open in normal and incognito mode, the credentials of Google and Outlook while it is open in incognito mode. Results show that the FTK gives better results than autopsy in terms of evidence extraction using hard-disk forensics.
    Keywords: browser artefacts; digital forensics; internet privacy; incognito mode; normal mode; FTK; autopsy.
    DOI: 10.1504/IJESDF.2024.10052836
  • Specific features of legal regulation of relations under the licence agreement   Order a copy of this article
    by Sayan Yesmaganbetov, Antonina Kizdarbekova, Botagoz Amanzholova, Aliya Nurzhanova, Nailya Akhmetova 
    Abstract: The subject of this paper is relevant due to the considerable number of intellectual works, both on the territory of Kazakhstan and in other countries. For the relations between the two parties to proceed competently from a legal standpoint, a system of licence agreements was created. An integral aspect of the study included the methods of scientific research, which enabled a deeper and more dynamic study of the variety of forms of the licence agreement and its impact on various aspects of the academic life of Kazakhstan and other countries. The results presented in this paper can be implemented in the analysis of difficulties arising in the settlement of legal aspects. The scientific originality of the work is the highlighting of the peculiarities of the legal regulation of relations under the license agreement.
    Keywords: legal standpoint; intellectual property; exclusive rights; intellectual rights; legal background; competent relationships.
    DOI: 10.1504/IJESDF.2023.10056467
  • Monitor and detect suspicious online transactions   Order a copy of this article
    by Swagata Sarkar, R. Babitha Lincy, P. Sasireka, Sonam Mittal 
    Abstract: This article provides a thorough examination of phishing attempts, their use, several contemporary visual similarity-based phishing detection systems, and their comparison evaluation. This research article aims to propose an effective design technique for IDS with regard to online applications. We develop a new set of features based on time-frequency analytics that makes use of 2-D models of monetary operations for preventing money laundering systems. As a classification algorithm, random forest is used, and clustering algorithm is used to tune the hyperparameters. Our findings imply that bitcoin exchanges would behave in an excessive reporting manner more than private banks under this law. We specifically take into account the monetary operations as a digital signal and attempt to build a classifier using a collection of frequently mined rules. Our tests on a replicated transaction dataset based on actual banking operations demonstrate the effectiveness of our suggested approach.
    Keywords: random forest technique; time frequency research; graphical study.
    DOI: 10.1504/IJESDF.2024.10052833
  • Private detective activity of the law enforcement system of Kazakhstan on the experience of foreign countries   Order a copy of this article
    by Yerbol Alimkulov, Assel Sharipova, Akynkozha Zhanibekov, Gulzhan Mukhamadiyeva, Aizhan Aryn 
    Abstract: The article's relevance is due to the need for a new look at the definition of the place of private detective activity in the system of law enforcement agencies. This article aims to introduce the ways that will help non-state private detective work not contradict the state professional law enforcement agencies. The general and special methods, including the dialectical method of cognition of real reality, were used. Historical-legal, structural-system, comparative-legal, logical, sociological, and statistical methods were used as well. This article provides a scientific justification for the consolidation of the Law of the Republic of Kazakhstan 'On private detective activity in the Republic of Kazakhstan'. As a result, scientific approaches to the organisation and legal regulation of private detective work have been developed. The provision of services by a private detective to citizens and organisations should be implemented in strict and obligatory compliance with national legislation and international standards.
    Keywords: private investigation; legal regulation; criminal proceedings; protection of rights; legislation; Kazakhstan.
    DOI: 10.1504/IJESDF.2023.10052564
  • An effective digital forensic paradigm for cloud computing criminal investigation   Order a copy of this article
    by Ravi Kumar, Kiran S. Kakade, M. Priscilla, B.V. Santhosh Krishna 
    Abstract: Cloud computing has been adopted by a wide variety of businesses and organisations to give services to customers in a secure and certified manner, protecting cloud providers from fraudulent actions. To investigate cloud-based cybercrimes, however, cost-effective forensics and successful implementation is essential. The topic has been the subject of several surveys and reviews thus far from researchers. An iCloud investigative tool taxonomy is presented in this study to find the products that meet their technical needs in a searchable catalogue. The authors of this study developed the taxonomy. The research results demonstrated that the recommended solution may effectively help digital inspectors in their mission to look into cloud-based cybercrimes. This research paper aims to analyse the digital forensics issues raised by the cloud computing paradigm and to offer the appropriate solutions and recommendations. Cloud computing and more conventional types of digital forensics are also given in-depth examination.
    Keywords: computer crimes; cloud technology; computer forensics; forensic investigations; forensic software tools; cloud crime; cloud forensics; cloud framework.
    DOI: 10.1504/IJESDF.2024.10052830