Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques

Hamouda, Djallel; Ferrag, Mohamed Amine; Benhamida, Nadjette; Seridi, Hamid; Ghanem, Mohamed Chahine

doi:10.1016/j.iot.2024.101149

Cited by 3 publications

References 24 publications

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Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

Khaleel,

Habeeb,

Albahri

et al. 2024

Journal of Intelligent Systems

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This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies and methodologies in adversarial attacks using machine learning (ML) and deep learning methods. A methodology was conducted to guarantee a thorough literature analysis of the studies using sources such as ScienceDirect, Scopus, IEEE Xplore, and Web of Science. A question was shaped to retrieve articles published from 2019 to April 2024, which ultimately produced a total of 704 papers. A rigorous screening, deduplication, and matching of the inclusion and exclusion criteria were followed, and hence 42 studies were included in the quantitative synthesis. The considered papers were categorized into a coherent and systematic classification including three categories: security enhancement techniques, adversarial attack strategies and defense mechanisms, and innovative security mechanisms and solutions. In this article, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth four challenges and motivations of adversarial attacks, while three recommendations have been discussed. A systematic science mapping analysis was also performed to reorganize and summarize the results of studies to address the issues of trustworthiness. Moreover, this research covers a large variety of network and cybersecurity applications of defense in adversarial attack subjects, including intrusion detection systems, anomaly detection, ML-based defenses, and cryptographic techniques. The relevant conclusions well demonstrate what have achieved in defense mechanisms against adversarial attacks. In addition, the analysis revealed a few emerging tendencies and deficiencies in the area to be remedied through better and more dependable mitigation methods against advanced persistent threats. The findings of this review have crucial implications for the community of researchers, practitioners, and policy makers in network and cybersecurity using artificial intelligence applications.

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Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

Khaleel,

Habeeb,

Albahri

et al. 2024

Journal of Intelligent Systems

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Generative AI for Threat Intelligence and Information Sharing

Sindiramutty,

V. Prabagaran,

Jhanjhi

et al. 2024

Advances in Digital Crime, Forensics, and Cyber Terrorism

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Collaboration in providing threat intelligence and disseminating information enables cyber security professionals to embrace digital security most successfully, whose risks are ever-changing. This article dwells on the capacity of machine intelligence to change information security by categorising indicators of compromise (IOC) and threat actors, then highlights the limits of traditional methods. Among Artificial intelligence tools such as generative adversarial networks (GANs) and Variational autoencoders (VAEs), which are the key innovators, one can create synthetic or fake threat data that emulates real attack scenarios in the past. This allows cyber-related risks to be analysed differently from before. In addition, this feature enables secure stakeholder collaborations. It is also meant mainly for factual data that protects private information but allows the exchange of helpful information. It is clear from the fact that showcasing real-world examples demonstrates Al's automation through cybersecurity detection.

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Generative AI for Threat Hunting and Behaviour Analysis

Sindiramutty,

Jhanjhi,

Akbar

et al. 2024

Advances in Digital Crime, Forensics, and Cyber Terrorism

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Cyber threats are becoming more advanced, and so is cybersecurity, which is getting more intellectual and better at hiding its presence. The requirement to achieve the balance between proactive resistive and threat-hunting measures in this dynamic environment is very high. Part four outlines how new AI techniques enable the design of the existing processes for hunting potential threats. The main objective is to digress into the core principles of threat hunting, starting from being proactive and including scenarios of deducing the clues based on the hypothesis. Then, the authors will highlight the limitations of conventional methods in detecting the gimmicks that fool even skilled hunters with an unseen threat that is smoking hiddenly in a never-ending evolutionary process. Two well-studied approaches for tackling these challenges are generative models like generative adversarial networks (GANs) and variational autoencoders (VAEs).

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Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques

Cited by 3 publications

References 24 publications

Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

Generative AI for Threat Intelligence and Information Sharing

Generative AI for Threat Hunting and Behaviour Analysis

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