Understanding AI Methods for Intrusion Detection and Cryptographic Leakage

Computer Science > Cryptography and Security [Submitted on 26 Mar 2026] Understanding AI Methods for Intrusion Detection and Cryptographic Leakage Reza Zilouchian, Micheal Chavez, Fernando Koch We investigate the role of artificial intelligence in cybersecurity by evaluating how machine learning techniques can detect malicious network activity and identify potential information leakage in cryptographic implementations. We conduct a series of experiments using the NSL-KDD and CIC-IDS datasets to evaluate intrusion detection performance across controlled and shifted data environments. Our results demonstrate that AI models can achieve near-perfect detection accuracy within stable network environment. However, their performance declines when exposed to fluctuating or previously unseen traffic patterns. We also observed that learned models identify patterns consistent with side-channel leakage, suggesting that AI can assist in uncovering implementation-level vulnerabilities. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.25826 [cs.CR]   (or arXiv:2603.25826v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.25826 Focus to learn more Submission history From: Reza Zilouchian [view email] [v1] Thu, 26 Mar 2026 18:42:57 UTC (416 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)