Forthcoming and Online First Articles

International Journal of Information and Computer Security

International Journal of Information and Computer Security (IJICS)

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International Journal of Information and Computer Security (28 papers in press)

Regular Issues

  • A Robust Feature Points Based Screen-shooting Resilient Watermarking Scheme   Order a copy of this article
    by Ruixia Yan, Yuan Jia, Lin Gao 
    Abstract: Screen-shooting will lead to information leakage. Anti screen-shooting watermark, which can track the leaking sources and protect the copyrights of images, plays an important role in image information security. Due to the randomness of shooting distance and angle, more robust watermark algorithms are needed to resist the mixed attack generated by screen-shooting. A robust digital watermarking algorithm that is resistant to screen-shooting is proposed in this paper. We use improved Harris-Laplace algorithm to detect the image feature points and embed the watermark into the feature domain. In this paper, all test images are selected on the dataset USC-SIPI and six related common algorithms are used for performance comparison. The experimental results show that within a certain range of shooting distance and angle, this algorithm presented can not only extract the watermark effectively but also ensure the most basic invisibility of watermark. Therefore, the algorithm has good robustness for anti screen-shooting.
    Keywords: blind watermarking; screen-shooting; robustness; invisibility; feature points; QR code; discrete cosine transform; DCT.
    DOI: 10.1504/IJICS.2023.10056328
     
  • Post-Quantum zk-SNARKs from QAPs   Order a copy of this article
    by Ken Naganuma, Masayuki Yoshino, Noboru Kunihiro, Atsuo Inoue, Yukinori Matsuoka, Mineaki Okazaki 
    Abstract: In recent years, the zero-knowledge proof and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) have drawn significant attention as privacy-enhancing technologies in various domains, especially the cryptocurrency industry and verifiable computations. rnA post-quantum designated verifier type zk-SNARK for Boolean circuits was proposed by Gennaro et al. in ACM CCS '18. However, this scheme does not include arithmetic circuits. Furthermore, it is difficult to use it in various applications. Their paper described the construction of a post-quantum designated verifier zk-SNARK for arithmetic circuits from quadratic arithmetic programs (QAPs) as an open problem. rnRecently, Nitulescu proposed a post-quantum designated verifier zk-SNARK for arithmetic circuits using square arithmetic programs (SAPs), which are the special cases of QAPs. rnIn this paper, we give other answers to this problem and propose rntwo post-quantum designated verifier zk-SNARK schemes for arithmetic circuits using QAPs. Our first proposal is based on the data structure used in Pinocchio, a previous study, and can be easily implemented using the existing Pinocchio-based systems. Furthermore, this scheme does not require strong security assumptions. rnIn our second proposal, which also employs QAPs, the zero-knowledge proof comprises three learning with errors (LWE) ciphertexts, and the size of the proof is smaller compared with that of the first proposal. Our second proposal is also more efficient than the first one or all other known post-quantum zk-SNARKs. rnWe implemented our proposed schemes and other known schemes using the libsnark library. Our experimental results show that the second scheme is faster than the previous post-quantum zk-SNARK schemes. rnThe second scheme can generate a zero-knowledge proof for an arithmetic circuit that comprises $2^{16}$ gates in a processing time of only 50 s, which is approximately three times faster than that of the post-quantum zk-SNARKs by Gennaro et al. or two times faster than the one by Nitulescu.
    Keywords: Zero-knowledge proof; zk-SNARKs; LWE encryption; Blockchain technology; Post-quantum cryptography.

  • Robust watermarking of Medical Images using SVM and hybrid DWT-SVD   Order a copy of this article
    by Kumari Suniti Singh, Harsh Vikram Singh 
    Abstract: In the present scenario, the security of medical images is an important aspect in the field of image processing. Support vector machines (SVMs) are a supervised machine learning technique used in image classification. The roots of SVM are from statistical learning theory. It has gained excellent significance because of its robust, accurate, and very effective algorithm, even though it was applied to a small set of training samples. SVM can classify data into binary classification or multiple classifications according to the application’s needs. Discrete wavelet transform (DWT) and singular value decomposition (SVD) transform techniques are utilised to enhance the image’s security. In this paper, the image is first classified using SVM into ROI and RONI, and thereafter, to enhance the images diagnostic capabilities, the DWT-SVD-based hybrid watermarking technique is utilised to embed the watermark in the RONI region. Overall, our work makes a significant contribution to the field of medical image security by presenting a novel and effective solution. The results are evaluated using both perceptual and imperceptibility testing using PSNR and SSIM parameters. Different attacks were introduced to the watermarked image, which shows the efficacy and robustness of the proposed algorithm.
    Keywords: support vector machine; SVM; discrete wavelet transform; DWT; singular value decomposition; SVD; watermark embedding; image watermarking.
    DOI: 10.1504/IJICS.2023.10057699
     
  • An Image Encryption Using Hybrid Grey Wolf Optimization and Chaotic Map   Order a copy of this article
    by Ali Akram Abdul-Kareem, Waleed Ameen Mahmoud Al-Jawher 
    Abstract: Image encryption is a critical and attractive issue in digital image processing that has gained approval and interest of many researchers in the world. A proposed hybrid encryption method was implemented by using the combination of the Nahrain chaotic map with a well-known optimised algorithm namely the grey wolf optimisation (GWO). It was noted from analysing the results of the experiments conducted on the new hybrid algorithm, that it gave strong resistance against expected statistical invasion as well as brute force. Several statistical analyses were carried out and showed that the average entropy of the encrypted images is near to its ideal information entropy.
    Keywords: cryptography; optimisation algorithm; grey wolf optimisation; GWO; chaotic system; chaos; security applications; secure communication.
    DOI: 10.1504/IJICS.2023.10057701
     
  • Efficient Multi-party Quantum Key Agreement Protocol Based on New Bell State Encoding Mode   Order a copy of this article
    by Zexi Li, Kefan Cheng, Yan Sun, Hongfeng Zhu 
    Abstract: Although there are many quantum key agreement protocols currently in existence, they cannot be merged in terms of resource utilisation, efficiency, security, and other aspects, and there are also significant differences in the nature of two and more parties. Therefore, it is necessary to design a quantum key agreement protocol that can balance efficiency and security and is suitable for multiple participants. In view of this, this paper proposes a multi-party quantum key agreement protocol based on a new coding mode of bell state: temporary session keys are negotiated between adjacent participants, and then shared keys for all participants are negotiated through the exchange, conversion, and computation of quantum resources. During the implementation of the protocol, not only can the identity of the participants be authenticated, but also the quantum resources used are single, and the quantum operations performed are simple. Moreover, efficiency is fixed and does not decrease due to the increase of participants or quantum resources. In addition, the protocol also allows participants to dynamically join and leave. In terms of security, the protocol can resist most common quantum attacks. Under the existing quantum technology, this protocol is completely feasible.
    Keywords: bell state encoding; multi-party; quantum key agreement; QKA; authentication.
    DOI: 10.1504/IJICS.2023.10057985
     
  • Secure Digital Academic Certificate Verification System using Blockchain   Order a copy of this article
    by Sunil Patel, Saravanan Chandran, Purushottam Kumar 
    Abstract: At present, there is a need for an authentic and fast approach to certificate verification. Which verifies and authenticates the certificates to reduce the extent of duplicity and time. An academic certificate is significant for students, the government, universities, and employers. Academic credentials play a vital role in the career of students. A few people manipulate academic documents for their benefit. There are cases identified where people produced fake academic certificates for jobs or higher education admission. Various research works are developing a secure model to verify genuine academic credentials. This research article proposed a new security model which contains several security algorithms such as timestamps, hash function, digital signature, steganography, and blockchain. The proposed model issues secure digital academic certificates. It enhanced security measures and automated educational certificate verification using blockchain technology. The advantages of the proposed model are automated, cost-effective, secured, traceable, accurate, and time-saving.
    Keywords: digital academic certificate; DAC; hash function; blockchain technology; digital signature; steganography.
    DOI: 10.1504/IJICS.2023.10058109
     
  • WTSEMal: A Malware Classification Scheme Based on Wavelet and SE-Resnet   Order a copy of this article
    by Dongwen Zhang, Shaohua Zhang, Guanghua Zhang, Naiwen Yu 
    Abstract: Aiming at the problem that traditional malware feature extraction data is huge and features are diverse, which requires lots of reverse engineering expertise and the detection effect is poor. In this study, we propose a visual malware classification scheme based on Wavelet and SE-Resnet network named WTSEMal. Firstly, convert the binary file of the malware sample into an image format. Then, after the image is pre-processed by normalisation, mean filtering and data augmentation, the image is decomposed and reorganised by wavelet transform (WT). Finally, the reconstructed image is input into SE-Resnet network for family classification. The experimental results show that the accuracy of the proposed WTSEMal classification scheme in malimg and Big15 is 99.22% and 97.49%, respectively, which are better than the existing machine learning malware classification methods. Compared with traditional classification methods, it has a good detection effect in detecting confusion or variant samples, and has strong generalisation ability.
    Keywords: malware detection; wavelet transform; WT; malware visualisation; deep learning.
    DOI: 10.1504/IJICS.2023.10058896
     
  • Feature-driven intrusion detection method based on improved CNN and LSTM   Order a copy of this article
    by Jing Zhang, Yufei Zhao, Jiawei Zhang, Lin Guo, Xiaoqin Zhang 
    Abstract: To make up the lack of detection capabilities of traditional machine learning methods. A network intrusion detection method based on improved convolutional neural network (CNN) and improved long and short-term memory network (HMLSTM) is proposed. The proposed method is mainly divided into four steps, namely data pre-processing, feature extraction, model training and detecting. First, we use the normalisation technology to pre-process the data; and then we use the lion swarm optimisation (LSO) algorithm to optimise the hyperparameters of the CNN to form the optimal CNN (OCNN) structure, and combine HMLSTM model to extract the spatial and temporal features. Finally, we use the spatial-temporal feature vectors to train and detect the upper classifier of OCNN-HMLSTM. This paper selects three commonly used datasets to do lots of experiments. The results show that the proposed method significantly improves the accuracy of network intrusion detection, and other metrics.
    Keywords: feature-driven; intrusion detection; convolutional neural network; CNN; long-short-term memory; LSTM.
    DOI: 10.1504/IJICS.2023.10059327
     
  • Machine Learning and Deep Learning Techniques for Detecting and Mitigating Cyber Threats in IoT-Enabled Smart Grids: A Comprehensive Review   Order a copy of this article
    by Aschalew Tirulo, Siddharth Chauhan, Kamlesh Dutta 
    Abstract: The confluence of the internet of things (IoT) with smart grids has ushered in a paradigm shift in energy management, promising unparalleled efficiency, economic robustness and unwavering reliability. However, this integrative evolution has concurrently amplified the grid’s susceptibility to cyber intrusions, casting shadows on its foundational security and structural integrity. Machine learning (ML) and deep learning (DL) emerge as beacons in this landscape, offering robust methodologies to navigate the intricate cybersecurity labyrinth of IoT-infused smart grids. While ML excels at sifting through voluminous data to identify and classify looming threats, DL delves deeper, crafting sophisticated models equipped to counteract avant-garde cyber offensives. Both of these techniques are united in their objective of leveraging intricate data patterns to provide real-time, actionable security intelligence. Yet, despite the revolutionary potential of ML and DL, the battle against the ceaselessly morphing cyber threat landscape is relentless. The pursuit of an impervious smart grid continues to be a collective odyssey. In this review, we embark on a scholarly exploration of ML and DL’s indispensable contributions to enhancing cybersecurity in IoT-centric smart grids. We meticulously dissect predominant cyber threats, critically assess extant security paradigms, and spotlight research frontiers yearning for deeper inquiry and innovation.
    Keywords: smart grid; cyber threats; cybersecurity; internet of things; IoT; deep learning; machine learning.
    DOI: 10.1504/IJICS.2024.10061784
     
  • An Intelligent Approach to Classify and Detection of Image forgery attack (Scaling and Cropping) using Transfer Learning   Order a copy of this article
    by Ravi Sheth, Chandresh Parekha 
    Abstract: Image forgery detection techniques refer to the process of detecting manipulated or altered images, which can be used for various purposes, including malicious intent or misinformation. Image forgery detection is a crucial task in digital image forensics, where researchers have developed various techniques to detect image forgery. These techniques can be broadly categorised into: active, passive, machine learning-based and hybrid. Active approaches involve embedding digital watermarks or signatures into the image during the creation process, which can later be used to detect any tampering. On the other hand, passive approaches rely on analysing the statistical properties of the image to detect any inconsistencies or irregularities that may indicate forgery. In this paper for the detection of scaling and cropping attack a deep learning method has been proposed using ResNet. The proposed method (Res-Net-Adam-Adam) is able to achieve highest amount of accuracy of 99.14% (0.9914) while detecting fake and real images.
    Keywords: image forgery; scaling; cropping; deep learning; transform learning; ResNet.
    DOI: 10.1504/IJICS.2024.10062129
     
  • A Novel Blockchain Consensus Protocol with Quantum Private Comparison for Internet of Vehicles   Order a copy of this article
    by Kefan Cheng, Lu Zhang, Yan Sun, Hongfeng Zhu 
    Abstract: Consensus protocols are a key feature in decentralised systems/networks which aiming to obtain and agree on a shared state among multiple unreliable nodes with diverse applications. Therefore, that integrated design with new technologies will become a difficult and hot research topic, especially in combining new fields such as quantum information and blockchain. Spontaneously, we propose a new consensus protocol in combination with quantum private comparison (QPC) in internet of vehicles (IoV) using practical Byzantine fault tolerance (PBFT) to achieve security and efficiency at higher levels. Through multi-node collaborative computing, different vehicles can quickly reach a consensus. More importantly, we have added quantum technology in the identity authentication and consensus phase, which can make our integrated network more robust and prevent malicious attacks. In other words, our protocol adopts QPC to make it impossible for any malicious node to maliciously disturb the order between nodes in the consensus phase, thus improving security. Finally, compared with the recent related literature, our consensus protocol has strong practicability and universality and can be well applied in the IoV environment.
    Keywords: quantum cryptography; quantum private comparison; internet of vehicles; IoV; blockchain.
    DOI: 10.1504/IJICS.2024.10062130
     
  • A Robust Intrusion Detection Techniques on Improved Features Selection Generalised Variable Precision Rough Set   Order a copy of this article
    by R. RAJESHWARI, M.P. Anuradha 
    Abstract: Network-based communication is becoming more and more susceptible as it is used extensively for outsiders and attacks in many areas. Intrusion detection is an essential process for a complete communication network security strategy. Intruders learn tactics of attacks every day, so they try to observe the significance of the intrusion detection system thoroughly, and they deny the services of IDS to the respective users. The three prominent roles that perform essential tasks in the network security of IDS are data collection, selection of optimal parameters, and classification made by decision-making engines. The recent research area highly relies on selecting an IDS optimal feature. Machine learning has explored various novel methods to improve performance and achieve a high accuracy rate. The proposed work implements a generalised rough set theory for optimal parameter selection, which leads to a formal way to enhance the accuracy. Support vector machines are used to classify network packet threats using machine learning. The suggested work uses the NSL-KDD dataset because it improves network communication security. Pre-processing data and feature selection on generic variable precision rough sets should be compared to best initial search and genetic search.
    Keywords: intrusion detection system; IDS; anomaly detection; generalised variable precision rough set; GVPRS; feature selection; machine learning; support vector machine; SVM; NSL-KDD dataset.
    DOI: 10.1504/IJICS.2024.10063042
     
  • Image Forgery Detection on Multi-Resolution Splicing Attacks using DCT and DWT   Order a copy of this article
    by Bhavani Ranbida, Debabala Swain, Bijay Paikaray 
    Abstract: Digital images play a vital role in this age of digitisation. Digital images can be easily forged by image editing tools intentionally or unintentionally. After forgery, these images are difficult to detect with the naked eye directly which creates social and legal troubles in feature vectors. Hence more efficient techniques need to be evolved that can easily detect the alterations in the digital image. Various methods have been proposed to carry out forensic analysis, but not so accurate and more time-consuming. In this paper we have proposed an innovative image forgery detection technique on copy-move and splicing attacks and the image authentication using discrete cosine transform (DCT) and discrete wavelet transform (DWT). The proposed technique detects the forgery regions in the images more accurately. The DCT and DWT techniques are mainly used for reduction in the dimension of the cover image and further partitioning into fixed sized non-overlapping blocks. This method significantly improves the detection of spliced area, the execution time, and accuracy result. Moreover, this technique is robust towards images with rotation, scaling, multiple copy-move forgery attack, splicing, etc. It provides a reliable and efficient solution for detection and ensuring image authenticity.
    Keywords: digital image forensic; multi-resolution; counterfeit detection; discrete wavelet transform; DWT.
    DOI: 10.1504/IJICS.2024.10063043
     
  • Unified Singular Protocol Flow for OAuth (USPFO) Ecosystem   Order a copy of this article
    by Jaimandeep Singh, Naveen Chaudhary 
    Abstract: OAuth 2.0 authorizes third-party clients to access a user's account on another app with limited permissions. The specification classifies clients by their ability to keep credentials confidential and grants different access types. This paper proposes USPFO, a new approach that combines different client and grant types into a unified protocol flow. USPFO can be used by both confidential and public clients to simplify the OAuth flow and reduce vulnerabilities. It also provides built-in protections against known OAuth 2.0 vulnerabilities such as client impersonation and token theft through integrity, authenticity, and audience binding. USPFO is compatible with existing RFCs, OAuth 2.0 extensions, and active internet drafts. By combining different client and grant types, USPFO streamlines the process and addresses the unique security and usability considerations for each type. This approach offers an alternative solution for OAuth providers looking to enhance their security and user experience.
    Keywords: OAuth 2.0 · USPFO · Unified Protocol Flow · Authorization Framework · Client Impersonation · Security; Vulnerabilities · Authentication · OAuth Extensions · Internet Standards.
    DOI: 10.1504/IJICS.2024.10063044
     
  • Data Dissemination and Policy Enforcement in Multi-Level Secure Multi-Domain Environments   Order a copy of this article
    by Joon Son, Essia Hamouda, Garo Pannosian, Vjay Bhuse 
    Abstract: Several challenges exist in disseminating multi-level secure (MLS) data in multi-domain environments. First, the security domains participating in data dissemination generally use different MLS labels and lattice structures. Second, when MLS data objects are transferred across multiple domains, there is a need for an agreed security policy that must be properly applied, and correctly enforced for the data objects. Moreover, the data sender may not be able to predetermine the data recipients located beyond its trust boundary. To address these challenges, we propose a new framework that enables secure dissemination and access of the data as intended by the owner. Our novel framework leverages simple public key infrastructure and active bundle, and allows domains to securely disseminate data without the need to repackage it for each domain.
    Keywords: active bundle; simple public key infrastructure; SPKI; mandatory access control; MAC; trust delegation; authorisation certificate.
    DOI: 10.1504/IJICS.2024.10063045
     
  • Contrast Enhancement in Probabilistic Visual Cryptography Schemes: A Pixel-Count based Approach   Order a copy of this article
    by Jisha T. E, Thomas Monoth 
    Abstract: The concerns with pixel expansion are eliminated by the introduction of size-invariant visual cryptography techniques. In the field of visual cryptography, the contrast of the decrypted image continues to be a hurdle. The two existing schemes in visual cryptography are the perfect reconstruction of black pixels and the perfect reconstruction of white pixels. In the current study, we introduce a size-invariant probabilistic technique, where the contrast of the deciphered image depends on the chosen scheme. Which scheme is employed depends on the total amount of black and white pixels in the covert image. Here, we’ve described the development and effectiveness of non-expanded probabilistic visual cryptography schemes with the perfect reconstruction of both black and white pixels that were based on several research studies. These schemes include (2, 2), (2, n), (n, n) and (k, n). We analysed the data using tables and charts to demonstrate the effectiveness of the suggested model, and we discovered that the projected models enhanced the contrast.
    Keywords: probabilistic; size-invariant; black and white pixels; visual quality; visual cryptography scheme.
    DOI: 10.1504/IJICS.2024.10064753
     
  • Cryptanalysis and Improvement of a Secure Communication Protocol for Smart Healthcare System   Order a copy of this article
    by Devender Kumar, Deepak Kumar Sharma, Parth Jain, Sumit Bhati, Amit Kumar 
    Abstract: There are many applications based on wireless technology and cloud computing in various fields. One such field that uses this technology is telemedicine or mobile healthcare. But with an increase in usage, these systems should be protected efficiently. Security is the greatest concern in this field. Recently, Sureshkumar et al. have proposed a protocol for a smart healthcare system, which uses 3-factor authentication. Here we cryptanalyze their scheme and find that it cannot withstand against the user impersonation attack, denial of service attack, privileged insider attack and gateway impersonation. To overcome these weaknesses, we propose an authentication protocol for smart healthcare system. To validate our claim, we use the ProVerif tool for formal security verification and compare our protocol with some related schemes. We also show that the proposed protocol is more secure and efficient than the related schemes.
    Keywords: User authentication; healthcare systems; denial of service attack; user impersonation attack; session key agreement; insider attack; sensor node.
    DOI: 10.1504/IJICS.2024.10064755
     
  • Blockchain-Based Composite Access Control and Secret Sharing Based Data Distribution for Security-Aware Deployments   Order a copy of this article
    by Kalyani Pampattiwar, Pallavi Chavan 
    Abstract: Securing cloud deployments includes patching and processing data from all input end-points that causes abnormal functioning and intrusions To incorporate security measures into cloud installations, many security models uses single or dual control mechanisms Cloud deployments are built on static rules, limiting their scalability to certain attack scenarios To address these limitations, this article presents a novel blockchain-based composite access control and secret sharing-based data distribution architecture for security-aware deployments The proposed model splits and merges sidechains using a Modified Genetic Algorithm Quality of Service awareness with federated deep learning improves model’s performance This approach combines swarm intelligence with secret sharing and provides dynamic as well as efficient data distribution in the cloud The model helps to mitigate Distributed Denial of Service, Finney, Man in the Middle, Sybil network attacks, SQL injection and query-based attacks The model’s Quality of Service performance is monitored and compared against state-of-the-art models.
    Keywords: Blockchain; Authentication; Access Control; Secret Sharing; Swarm intelligence; Federated Learning; Cloud; Genetic Algorithm; Quality of Service; Security.
    DOI: 10.1504/IJICS.2024.10064756
     
  • Robust and Secure File Transmission Through Video Streaming Using Steganography and Blockchain   Order a copy of this article
    by Xiangning Liang, Pushpendu Kar 
    Abstract: Videoconferencing software is widely used for online meetings. As a common sub-function, file transfer is always handled by a separate service, sometimes it is a third-party service. File transmission is usually developed upon well-known protocols for a typical commercial videoconferencing application. When sending files during a video session, file data flow and video stream are independent of each other. Encryption is a mature method to ensure file security, which is proved by years of industrial practice. However, it still has the chance to leave footprints on the intermediate forwarding machines. These footprints can indicate that a file once passed through, some protocol-related logs give clues to the hackers later investigation. In cases where higher security requirements are needed, it is better to avoid leaving footprints about file transmission in the network. This work proposes a file-sending scheme through the video stream using blockchain and steganography.
    Keywords: Video streaming; Blockchain; Steganography; File Transmission; Network Flow; File Security.
    DOI: 10.1504/IJICS.2024.10064757
     
  • IDMS Quantum Password-Authenticated Key Exchange Protocols   Order a copy of this article
    by Lu Zhang, Yan Sun, Yingfei Xu, Hongfeng Zhu 
    Abstract: In this paper, we design an ID-based M-server quantum password-authenticated key exchange scheme, where the client computes a strong key from its password and splits the key into m portions, and then encrypts them and sends them to m servers to be used as the basis for encryption and decryption in the subsequent key exchange process. The adoption of multiple servers can effectively prevent third-party attacks on the server and ensure the security of the key information, which is just like a complex secret sharing mechanism in traditional computational cryptography, for example, secret sharing (m, n) threshold scheme, but our new quantum fusion technology to realise the secret sharing mechanism is more efficient and simpler. Finally, through analysis, our scheme can meet most of the security requirements and perform well. It is feasible to implement the protocol under the existing quantum technology.
    Keywords: quantum technology; password-authenticated key exchange; secret sharing; multiple servers.
    DOI: 10.1504/IJICS.2024.10064758
     
  • The APT Family Classification System Based on APT Call Sequences and Attention Mechanism   Order a copy of this article
    by Zeng Shou, Yue-bin Di, Xiao Ma, Rui-chao Xu, He-qiu Chai, Long Yin 
    Abstract: Among the many cyber attack activities, Advanced Persistent Threat (APT) has caused more serious impact on enterprises, and the malware used by hacker groups is also very complex, which poses a great obstacle to analyze and trace the source However, malware used by the same hacker group is internally correlated, and there are differences in malware between different hacker groups Currently, deep learning has achieved results in many fields, and its application in the security field is becoming more and more widespread In this paper, we design an APT family classification system based on API call sequences, which extracts API call sequences from malware and uses a one-dimensional convolutional neural network with attention mechanism for classification The system is tested on a test dataset of 12 different families of 12 different families of malware, and the test results show that the system has high accuracy as well as practicality.
    Keywords: APT; Dynamic Analysis; Convolutional Neural Network.
    DOI: 10.1504/IJICS.2024.10064759
     

Special Issue on: Security and Privacy for Emerging Technology

  • The Relationship between Digital Information Security of the Supply Chain and Enterprise Development   Order a copy of this article
    by Zhezhou Li, Xiangrong Kong, Xiaozhen Jiang 
    Abstract: This study aims to enhance the core competitiveness of enterprises in the competitive environment and realise the rapid and sound development of enterprise security. The relationship between the digital transformation of the supply chain and the core competitiveness of enterprises is discussed from the perspective of constructing the information security (IS) of the internet of things (IoT). Firstly, the ciphertext-policy attribute-based encryption (CP-ABE) model of the information centre is established to study the technical problems of information encryption of IoT enterprises. Secondly, the correlation analysis method is used to determine the impact of supply chain transformation on the future development of enterprises through the correlation between the digital transformation of the supply chain and enterprise competitiveness. Finally, targeted solutions are proposed for the digital transformation of the supply chain and the IS of IoT of enterprises.
    Keywords: information security of the internet of things; supply chain digital transformation; algorithm response time; core competitiveness of enterprises; correlation analysis.
    DOI: 10.1504/IJICS.2023.10057702
     
  • Encryption by block based on rekeying and inter-intra pixel permutation   Order a copy of this article
    by Rachid RIMANI, Adda Ali Pacha, Naima HADJ SAID 
    Abstract: The growing use of ICT exposes exchanges to certain risks, which require the existence of adequate security measures of information. The data encryption is often an effective way to meet these requirements of security and confidentiality. This paper present a novel cryptosystem by block depended on the secret key and subkeys and respect the fundamental principles of modern cryptography for encrypting images, since images have some intrinsic features such as large data capacity, strong correlation between adjacent pixels and high redundancy of information. The proposed cryptosystem is based on rekeying to apply permutation inter-intra pixel of each block according to the secret key and subkeys using a random, nonlinear, dynamic and secret scrambled technique there be called RNDSS. Experimental tests demonstrate the high security of the proposed cryptosystem with RNDSS technique and show the high sensitive dependence to any subtle change in the secret key, which guarantees the security against brute force attacks.
    Keywords: cryptosystem by block; permutation inter-intra pixel; rekeying; RNDSS technique; secret key.
    DOI: 10.1504/IJICS.2024.10061312
     
  • Enterprise Intelligent Financial Sharing Mechanism in the Security Environment of the Internet of Things   Order a copy of this article
    by Yongling Zhang, Xuandong Zhang, Jinlong Song 
    Abstract: This study aims to enhance the functional development of enterprise intelligent financial sharing and ensure the security of financial information transmission within organisations. It begins by comprehensively understanding the current state of the internet of things (IoT) security environment and the establishment of enterprise intelligent financial sharing mechanisms. Subsequently, this study analyses and discusses the continuous identity authentication and security threat assessment control model in the context of IoT security under decentralised computing. Finally, the grey clustering trigonometric function evaluation model is employed to establish a functional evaluation mechanism for enterprise intelligent financial sharing, thereby improving the functionality of financial sharing. The findings indicate that IoT data exhibits the highest levels of security and reliability. By continuously controlling the attack rate below 0.2, the number of malicious nodes is significantly reduced, leading to a basic guarantee of IoT information security.
    Keywords: sharing of intelligent enterprise finance; internet of things security; continuous identity authentication; indicator integration; grey clustering.
    DOI: 10.1504/IJICS.2024.10061313
     
  • Library Data Protection and Threat Detection System Based on Network Security   Order a copy of this article
    by Jianxin Xiong, Xianping Wang 
    Abstract: Traditional libraries may have security risks such as data breach, network attack and virus infection, which requires a library data protection and threat detection system based on network security to effectively manage and protect. Based on the premise of network security, this article focused on analysing library data protection and threat detection, constructing a library data protection and threat detection system using encryption algorithms, and testing its performance. According to the experimental results, it can be concluded that the average response time of unknown threats in the library data protection and threat detection system based on network security ranged from 0.4s to 0.8s under different test times. It can be seen that the system not only performs well, but also has very good user satisfaction. This article aimed to ensure the security of users’ use of library resources and services through effective threat detection and data protection measures.
    Keywords: library data protection; threat detection; network security; encryption algorithm; security vulnerability scanning; data breach.
    DOI: 10.1504/IJICS.2024.10061565
     
  • Analysis of Competitive Differences in the Bilateral Platforms of the Digital Economy Using Artificial Intelligence and Network Data Security   Order a copy of this article
    by Jingfeng Jiang, Yongyi Wu 
    Abstract: This work aims to conduct a more precise and secure analysis of the competitive differences in bilateral platforms of the digital economy based on AI and network data security. The application of Artificial Intelligence (AI) technology on bilateral platforms can significantly enhance the level of intelligence and personalization in services. However, it also drives continuous advancements in data security technology. This work explores the characteristics and advantages of different platforms, providing decision support and strategic guidance to relevant institutions and businesses. Based on this, this work first establishes a distributed training scheme under fog computing to counter data poisoning and safeguard privacy. This scheme is utilized for data collection, storage, and processing while incorporating stringent measures for data privacy protection, such as data encryption, identity authentication, and access control.
    Keywords: artificial intelligence; network data security; digital economy; bilateral platforms; analysis of competitiveness differences.
    DOI: 10.1504/IJICS.2024.10061566
     
  • The Optimization of Enterprise Internet of Things Security Management System under Digital Economy   Order a copy of this article
    by Jianhua Liu, Huijie Ma 
    Abstract: In the context of the digital economy, this study integrates the Internet of Things (IoT), blockchain, ant colony optimization (ACO), neural network, and other modern digital information technologies. An enterprise IoT Security Management System (SMS) is built to address the security risks of enterprise IoT information and data. The improved backpropagation (BP) algorithm optimizes data transactions and network security templates in enterprise IoT SMS. At the same time, this study also introduces the ACO to improve the performance of the BP neural network. Firstly, the methods of enterprise IoT security management under the growth of the digital economy and the technical path of digital technology for enterprise security management are explained. Secondly, a BP algorithm based on an ant colony algorithm and genetic algorithm optimization is established to improve the speed and security of data transactions and network security module processing information data in the enterprise IoT SMS.
    Keywords: Digital economy; Internet of Things; Blockchain; Enterprise security management; System optimization; Ant colony optimization.
    DOI: 10.1504/IJICS.2024.10061567
     
  • Anomaly based Intrusion Detection System Using Harris Hawks optimization with a Sigmoid Neuron Network   Order a copy of this article
    by LENIN NARENGBAM, Shouvik Dey 
    Abstract: This study introduces an innovative approach, merging Harris Hawks Optimization (HHO) with a sigmoid neuron network (SN), to enhance anomaly-based intrusion detection systems (ADS) performance. The resultant SN-HHO hybrid model aims to elevate detection rates and lower false positive rates (FPRs) within ADS. Evaluation across five datasets - UNSW-NB15, CIDDS-001, NSL-KDD, AWID3, and CICDDoS2019 - reveals heightened accuracy and faster convergence compared to existing methods. This work underscores the potential synergy of meta-heuristic optimization and artificial neural networks, offering a promising strategy to fortify IDS performance and reliability, thus presenting a novel direction for advancing anomaly detection practices.
    Keywords: Intrusion Detection System; Neural Network; Meta-heuristic Optimization; Machine Learning.
    DOI: 10.1504/IJICS.2024.10064754