EVALUATION OF AI ATTACK MITIGATION FROM CITRIX BLEED TO SELF-EVOLVING MALWARE: MODERNISING AEROSPACE CYBER DEFENCE WITH AI

EVALUATION OF AI ATTACK MITIGATION FROM CITRIX BLEED TO SELF-EVOLVING MALWARE: MODERNISING AEROSPACE CYBER DEFENCE WITH AI | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 7 July 2025 V1 Latest version Share on EVALUATION OF AI ATTACK MITIGATION FROM CITRIX BLEED TO SELF-EVOLVING MALWARE: MODERNISING AEROSPACE CYBER DEFENCE WITH AI Authors : Daniel Schönle 0000-0001-8094-0809 [email protected] and Christoph Reich Authors Info & Affiliations https://doi.org/10.22541/au.175192375.52841126/v1 479 views 327 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study evaluates the effectiveness of cybersecurity architectures in mitigating AI-driven cyberattacks. A key tool in this evaluation is the Layered Rubric Security Score (LRS), a structured threat model-based MITRE ATT&CK framework that has been developed specifically for this purpose. LRS utilises architectural layers, cross-detection, containment, and automated response capabilities for assessment. Modern reference architectures from aerospace enterprises, designed to mitigate attacks like Citrix Bleed and RedLine Stealer, are assessed and compared. Findings reveal significant gaps in handling adaptive and LLM-based threats, particularly in the integration of CASB and EDR/XDR. LRS introduces a scoring methodology and proposes mitigation strategies aligned with Zero Trust Architecture, supporting the evidence-based improvement of AI-resilient defence infrastructures. Supplementary Material File (eeam_evaluation_of_ai_attack_mitigation.pdf) Download 2.22 MB Information & Authors Information Version history V1 Version 1 07 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords adaptive malware ai-enabled zero trust casb mitigation response automation ueba Authors Affiliations Daniel Schönle 0000-0001-8094-0809 [email protected] IDACUS Insitute Furtwangen University View all articles by this author Christoph Reich IDACUS Insitute Furtwangen University View all articles by this author Metrics & Citations Metrics Article Usage 479 views 327 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Daniel Schönle, Christoph Reich. EVALUATION OF AI ATTACK MITIGATION FROM CITRIX BLEED TO SELF-EVOLVING MALWARE: MODERNISING AEROSPACE CYBER DEFENCE WITH AI. Authorea . 07 July 2025. DOI: https://doi.org/10.22541/au.175192375.52841126/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 Abdullah Al Siam, Moutaz Alazab, Areej Obeidat, Nuruzzaman Faruqui, Somaya Al-Maadeed, TransCall: A Transformer-Driven Framework for Zero-Day Malware Detection Using System Call Sequences, 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), (1-6), (2026). https://doi.org/10.1109/ICAIC67076.2026.11395707 Crossref Asem Daoud, Mohamed Hamdi, AI-Powered Detection of Advanced Persistent Threats (APTs): A Decision Tree Model for Intrusion Detection Using MITRE ATT&CK Behavioral Analysis, Journal of Advances in Information Technology, 17 , 5, (895-913), (2026). https://doi.org/10.12720/jait.17.5.895-913 Crossref Anil Kumar Pakina, Akhtaruzzaman Khan, Aryendra Dalal, Keshav Kaushik, Renu Kumawat, Mukesh Soni, Integrating AI-Driven Cybersecurity Models with Automated Digital Forensics for NextGeneration Defense, 2025 2nd International Conference on Intelligent Systems for Cybersecurity (ISCS), (1-7), (2025). https://doi.org/10.1109/ISCS69371.2025.11386029 Crossref Muna A. Radhi, Majd S. Ahmed, Ethar Abdul Wahhab Hachim, Zeyad Farooq Lutfi, Emerging Trends and AI-Driven Defense Mechanisms in Cybersecurity: A Novel Framework for Threat Prediction and Prevention, CyberSystem Journal, 2 , 1, (10-21), (2025). https://doi.org/10.57238/csj.2025.1002 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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