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Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications

arXiv Security Archived 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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    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 Focus to learn more Submission history From: Abdeljebar Mansour Prof. [view email] [v1] Sun, 17 May 2026 01:44:23 UTC (26 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.AI cs.LG cs.NI eess eess.SP 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?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    May 19, 2026
    Archived
    May 19, 2026
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