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International journal of electronic security and digital forensics: IJESDF
Inderscience Enterprises Ltd.
International journal of electronic security and digital forensics: IJESDF

Inderscience Enterprises Ltd.

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1751-911X

International journal of electronic security and digital forensics: IJESDF/Journal International journal of electronic security and digital forensics: IJESDFEIESCI
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    DarkExtract: tool for extracting and analysing Tor Browser host-based activities

    Mandela N.Mahmoud A.A.S.Mistry N.R.Agrawal A.K....
    563-581页
    查看更多>>摘要:Copyright © 2025 Inderscience Enterprises Ltd.The increasing usage of Tor Browser, a popular tool for anonymous web browsing, has presented unique challenges for forensic investigators in analysing digital evidence. This research paper introduces Dark_Extract, an open-source tool designed to simplify the identification and analysis of host-based artefacts left by Tor Browser. The purpose of this study is to address the challenges associated with forensic analysis of Tor Browser traces by providing a user-friendly and efficient solution. The methodology employed in developing Dark_Extract involved the analysis of Tor Browser’s architecture and the identification of key host-based artefacts relevant to forensic investigation. The tool was then developed to automate the extraction and analysis of these artefacts, eliminating the need for extensive knowledge of Tor Browser’s intricate structure. The major findings of this study demonstrate the effectiveness of Dark_Extract in simplifying the forensic analysis of Tor Browser traces. The tool successfully extracts and presents crucial host-based artifacts such as downloads, cookies, browsing history, and bookmarks, which can be of significant importance in forensic investigations. The results obtained through the use of Dark_Extract indicate its accuracy and efficiency in identifying and organising these artefacts.

    A model for detecting cyber security intrusions using machine learning techniques

    Baptist L.J.Selvam J.Chakkaravarthy D.M.
    582-592页
    查看更多>>摘要:Copyright © 2025 Inderscience Enterprises Ltd.Because hackers are using more sophisticated methods, the number of cyberattacks is rising at an alarming rate. In addition, maintaining adequate levels of cyber security is becoming more difficult on a daily basis due to the prevalence of malicious actors carrying out cyberattacks in the modern digital environment. Therefore, in order to have a safe network, it is required to establish privacy and security measures for the systems. A substantial amount of further research is required in the domain of intrusion detection. This study introduces an intrusion detection tree (referred to as ‘IntruDTree’), which is a security model based on machine learning. Ultimately, the efficacy of the IntruDTree model was assessed by the execution of tests on many cybersecurity datasets. To assess the efficacy of the resulting security model, we conduct a comparative analysis between the outputs of the IntruDTree model and those of other well-established machine learning techniques.

    The shifting landscape: a comprehensive examination of the top five emerging cybersecurity threats

    Ibrar M.Li H.Zhang W.Yin S....
    593-603页
    查看更多>>摘要:Copyright © 2025 Inderscience Enterprises Ltd.The phrase ‘cybersecurity’ describes an atmosphere that can keep hackers out while safeguarding computers, networks, and sensitive data. It is a set of methods, procedures, and practices for preventing data breaches and other forms of cyber-crime. As cyber risks have multiplied, the field of cybersecurity has expanded rapidly in recent years. Firewalls, encryption, strong passwords, and intrusion detection and response systems are all crucial components of a secure digital infrastructure. These strategies must be taught to workers. To protect sensitive information from cybercrime, businesses, organisations, and individuals need to be aware of the five most serious concerns now facing the cybersecurity industry. The article concluded that more excellent knowledge of these risks is crucial to successfully managing digital environments and defending them from electronic hazards.

    Cloud computing’s multi-key privacy-preserving deep learning system

    Mani A.Shanmuganathan M.Lincy R.B.Rubia J.J....
    604-615页
    查看更多>>摘要:Copyright © 2025 Inderscience Enterprises Ltd.Many fields have seen success with deep learning implementations, including bioinformatics, photo processing, gaming, computer security, etc. However, a large amount of training data is typically required for deep learning, which may not be made available by a single owner. As the amount of data continues to rise at an exponential rate, many people are turning to remote cloud services to store their information. Human activity recognition (HAR) provides massive amounts of data from IoT devices to collaboratively construct predictive models for medical diagnosis. To protect users’ anonymity in scenarios where DNNs are used in HAR learning, we present Multi-Scheme Differential Privacy. MSDP uses a multi-party, secure variant of the ReLU function to cut down on transmission and processing time. MSDP is proved to be secure in comparison to existing state-of-the-art models without compromising privacy through experimental validation on four of the most popular human activity detection datasets.

    Image encryption and decryption using graph theory

    Kumar S.Chandrasekaran S.Manogaran N.Krishna B....
    616-630页
    查看更多>>摘要:Copyright © 2025 Inderscience Enterprises Ltd.Through the application of the ideas presented in graph theory, this work presents a novel approach to the protection of picture data. The approach that has been developed takes into account the pixels that make up the digital picture as vertices of a network and forms edges between the vertices, assigning a certain amount of meaningful weight to each connection. The encryption and decryption procedure for the colour digital picture is presented. This approach makes use of the minimum spanning tree (MST) and the weighted adjacency matrix of the MST. For the purpose of validating the practicability and robustness of the suggested approach, the experimental findings and the security analysis of the proposed methodology are presented. In order to demonstrate that the suggested approach is resistant to statistical assaults, statistical analysis techniques such as histogram, correlation, and entropy were used. It has also been shown via the results of the experiments that the suggested method is resistant to assaults including brute force and occlusion.

    Enhancing network security: a deep learning-based method to detect and diminish attacks

    Franklin Jino R.E.Paulsamy A.M.Shanmugam G.Vishwakarma R.K....
    631-645页
    查看更多>>摘要:Copyright © 2025 Inderscience Enterprises Ltd.In today’s world, preventing illegal intrusions into communication networks is an absolute need in order to protect the personal information of users and maintain the integrity of their data. The establishment of intrusion detection systems (IDS) and the improvement of their accuracy have both been shown to benefit from the use of data mining and machine learning techniques. We make use of the well-known AWID3 dataset, which contains traffic from wireless networks. The Krack and Kr00k attacks, which are aimed at the most serious vulnerabilities in the IEEE 802.11 protocols, are the primary focus of our research and development efforts. This success rate was reached by our ensemble classifier. When it came to identifying instances of the Kr00k attack, our neural network-based model had a high accuracy rate of 96.7%, which further emphasised the usefulness of the remedy that we suggested.

    Image encryption using artificial intelligence algorithms for secure communication

    Kumarganesh S.Jennifer D.Ramesh B.Elango S....
    646-656页
    查看更多>>摘要:Copyright © 2025 Inderscience Enterprises Ltd.Within the context of today’s networks and massive amounts of data, the safe transmission of digital photographs is met with a myriad of formidable obstacles. We provide an adaptable framework with the goal of maintaining the privacy and safety of photographs that are delivered over an electronic healthcare system. In our technique, the 3D-chaotic system is used to produce a keystream, which is then applied to the picture to achieve 8-bit and 2-bit permutations respectively. The efficacy of the proposed encryption method is shown using histogram analysis, neighbouring pixel correlation analysis, anti-noise attack analysis, and resistance to occlusion attack analysis. The technique of encryption offers a number of benefits, including a high volume of information that may be encrypted, strong resilience, and a quick decryption time. This demonstrates that the scheme is capable of withstanding statistical attacks and suitable for use as a security framework in AI-based healthcare.

    Improving reliability with wormhole detection for mobile routing to enhance network security

    Lakshmi V.V.Akash B.Manesh M.Praveen A....
    657-665页
    查看更多>>摘要:Copyright © 2025 Inderscience Enterprises Ltd.Problems with mobile routing in lossy, low-power networks (LLNs) are examined in this paper. The IPv6 routing protocol for low-power and lossy networks (RPL), which is the IPv6 standard routing protocol for LLNs, has mostly been investigated in static LLNs and does not clearly contain a mobility support mechanism. The IPv6 over low power wireless personal area networks (6LoWPAN) protocol, which aims to make it possible to transmit IPv6 packets over low-power wireless networks, is one example of an LLN protocol. Routing protocol for low-power and lossy networks (RPL) and constrained application protocol are other LLN protocols (CoAP). A node in an ad hoc network collects packets from one location and retransmits them to another using a long-distance link inside the network. This method determines the wormhole link by computing the largest end-to-end latency between any two nodes in the communication range.

    Application of audio-video data embedding approach to increase imperceptibility and robustness using forensic detection

    Moon S.K.
    666-687页
    查看更多>>摘要:Copyright © 2025 Inderscience Enterprises Ltd.Data hiding using steganography plays a very important role in providing security, privacy, authentication, and robustness of secret data. In today’s digital world, the significance of safeguarding data privacy and its authentication is very crucial and risky. There are many ways to protect and safeguard the security, and privacy of secret data using steganography techniques but, all this steganography approaches are used to embed secret data like images, text, and audio which provide less privacy, and data security. This paper implemented the multi pixel exploiting modification direction (MP-EMD) approach on video where three pixels of any frames are used to embed secret data at a time using forensics detection technique. The simulated and verified results verify the better privacy and safeguarding of the secret data, peak signal noise ratio (PSNR), correlation indicator (CI), robustness, and embedding capacity (EC) as compared to any existing methods.