Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications
arXiv SecurityArchived May 19, 2026✓ Full text saved
arXiv:2605.17219v1 Announce Type: new Abstract: Artificial Intelligence (AI) is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various applications. Its integration into Cybersecurity has garnered significant attention, particularly in areas such as intrusion detection, malware analysis, and phishing or spam detection. As AI and cybersecurity evolve, new methods and approaches emerge regularly. Current trends include the use of Generative
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✦ AI Summary· Claude Sonnet
Computer Science > Cryptography and Security
[Submitted on 17 May 2026]
Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications
S. Tazili, A. Mansour, M. Y. Chkouri
Artificial Intelligence (AI) is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various applications. Its integration into Cybersecurity has garnered significant attention, particularly in areas such as intrusion detection, malware analysis, and phishing or spam detection. As AI and cybersecurity evolve, new methods and approaches emerge regularly. Current trends include the use of Generative AI, Natural Language Processing, Federated Learning for privacy-preserving collaborative training, and eXplainable AI to ensure interpretability and trust, which are vital in cybersecurity. This paper presents an interesting review of current AI-based cybersecurity trends, focusing on intrusion detection approaches and aiming to uncover meaningful insights through comparative analysis based on the employed AI techniques and reported performance.
Comments: Accepted at AI2SD 2025. Forthcoming in Springer Lecture Notes in Networks and Systems (2026). Please cite this preprint as indicated in the paper!
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
Cite as: arXiv:2605.17219 [cs.CR]
(or arXiv:2605.17219v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2605.17219
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From: Abdeljebar Mansour Prof. [view email]
[v1] Sun, 17 May 2026 01:44:23 UTC (26 KB)
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