Adversarial AI in Cybersecurity: How Machine Learning Can Both Attack and Defend Digital Systems

Adversarial AI in Cybersecurity: How Machine Learning Can Both Attack and Defend Digital Systems | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 March 2025 V1 Latest version Share on Adversarial AI in Cybersecurity: How Machine Learning Can Both Attack and Defend Digital Systems Author : Tahir Abbas 0009-0007-0787-8093 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174129466.68069170/v1 493 views 154 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Adversarial AI is an emerging threat in cybersecurity, where machine learning (ML) techniques are exploited to both attack and defend digital systems. Malicious actors leverage adversarial ML to manipulate AI models, evade detection, and compromise security protocols. Conversely, cybersecurity professionals use ML to develop robust defense mechanisms that detect, mitigate, and counteract these attacks. This dual nature of AI-driven cybersecurity creates a continuous arms race between attackers and defenders. Key adversarial strategies include evasion attacks, data poisoning, and model inversion, which expose vulnerabilities in AI systems. To counter these threats, defensive approaches such as adversarial training, anomaly detection, and explainable AI enhance system resilience. As AI continues to evolve, the integration of adaptive security frameworks, threat intelligence, and ethical AI practices becomes crucial in safeguarding digital infrastructures. The study of adversarial AI highlights the need for continuous innovation in cybersecurity to address emerging risks in an increasingly automated and AI-dependent digital landscape. Supplementary Material File (6.pdf) Download 92.43 KB Information & Authors Information Version history V1 Version 1 06 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords adversarial ai adversarial attacks adversarial defense anomaly detection cybersecurity data poisoning evasion attacks machine learning model inversion Authors Affiliations Tahir Abbas 0009-0007-0787-8093 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 493 views 154 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Tahir Abbas. Adversarial AI in Cybersecurity: How Machine Learning Can Both Attack and Defend Digital Systems. Authorea . 06 March 2025. DOI: https://doi.org/10.22541/au.174129466.68069170/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Srinivasarao Dharmireddi, Ammar Hameed, Mustafa Albdairi, Eko G. Samudro, Manish Nandy, Cybersecurity in Digital Finance: Artificial Intelligence-Powered Fraud Detection and Risk Management, 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES), (1-5), (2025). https://doi.org/10.1109/ICCIES63851.2025.11032566 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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