Understanding AI Methods for Intrusion Detection and Cryptographic Leakage
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866912989245865984 |
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| author | Zilouchian, Reza Chavez, Michael Koch, Fernando |
| author_facet | Zilouchian, Reza Chavez, Michael Koch, Fernando |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25826 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Understanding AI Methods for Intrusion Detection and Cryptographic Leakage Zilouchian, Reza Chavez, Michael Koch, Fernando Cryptography and Security 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. |
| title | Understanding AI Methods for Intrusion Detection and Cryptographic Leakage |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2603.25826 |