🤖 AI Summary
This study investigates the robustness of artificial intelligence in two critical security tasks: network intrusion detection and identification of side-channel information leakage in cryptographic implementations. To address performance degradation under distribution shifts and unknown traffic scenarios, we systematically evaluate multiple machine learning approaches on the NSL-KDD and CIC-IDS datasets. Our results demonstrate that models achieve near-perfect detection accuracy in stable environments but suffer significant performance drops under distributional shifts. Furthermore, AI methods effectively identify feature patterns consistent with side-channel leakage, confirming their potential for security analysis of cryptographic implementations. This work provides an empirical foundation and methodological insights for enhancing the generalization capabilities of AI-driven security systems.
📝 Abstract
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.