Algorithmic Bias in Machine Learning-Based Cyber Defence: Taxonomy, Mathematical Frameworks, and Ethical Governance

Algorithmic Bias in Machine Learning-Based Cyber Defence: Taxonomy, Mathematical Frameworks, and Ethical Governance | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 16 March 2026 V1 Latest version Share on Algorithmic Bias in Machine Learning-Based Cyber Defence: Taxonomy, Mathematical Frameworks, and Ethical Governance Author : Joseph Foley 0009-0000-9220-0082 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177369010.03054587/v1 198 views 99 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract As Artificial Intelligence (AI) becomes the operational backbone of Security Operations Centres (SOCs), algorithmic bias poses a dual threat: technical failure and ethical violation. This study expands the taxonomy of bias within AI-driven cybersecurity systems, focusing on Intrusion Detection Systems (IDS) and Automated Threat Hunting pipelines. Drawing on peer-reviewed literature from 2015 to 2026, it analyses the mathematical foundations of fairness constraints-including Equalised Odds [14] and Predictive Parity-and their application to real-time network anomaly detection. Mitigation strategies across the entire machine learning pipeline (pre-processing, in-processing, and post-processing) [7] are surveyed. The role of Explainable AI (XAI) methods, specifically SHAP [18] and LIME [27], as safeguards against bias is evaluated. Governance implications of Regulation (EU) 2024/1689 (EU AI Act) [25] and the NIST AI Risk Management Framework (AI RMF) [24] are examined. Additionally, adversarial bias injection-including data poisoning attacks in Federated Learning [29] is explored as an emerging offensive vector. The synthesis demonstrates that fairness is not only a social imperative but also a fundamental component of system robustness, and that biased models are exploitable. Supplementary Material File (algorithmic_bias_in_machine_learning_based_cyber_defense__taxonomy__mathematical_frameworks__and_ethical_governance.pdf) Download 206.60 KB Information & Authors Information Version history V1 Version 1 16 March 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords algorithmic bias data poisoning eu ai act explainable ai fairness federated learning intrusion detection systems machine learning security operations centre shap Authors Affiliations Joseph Foley 0009-0000-9220-0082 [email protected] Munster Technological University View all articles by this author Metrics & Citations Metrics Article Usage 198 views 99 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Joseph Foley. Algorithmic Bias in Machine Learning-Based Cyber Defence: Taxonomy, Mathematical Frameworks, and Ethical Governance. Authorea . 16 March 2026. DOI: https://doi.org/10.22541/au.177369010.03054587/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')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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