Allam, Fatima Zohra, Bousbia-Salah, Hicham, Zendaoui, Fairouz, Hamami-Mitiche, Latifa. (1404). AI-Enhanced Intrusion Detection: Integrating Expert Knowledge and Machine Learning for Enterprise Networks. , 17(4), 99-116. doi: 10.22059/jitm.2025.105485
Fatima Zohra Allam; Hicham Bousbia-Salah; Fairouz Zendaoui; Latifa Hamami-Mitiche. "AI-Enhanced Intrusion Detection: Integrating Expert Knowledge and Machine Learning for Enterprise Networks". , 17, 4, 1404, 99-116. doi: 10.22059/jitm.2025.105485
Allam, Fatima Zohra, Bousbia-Salah, Hicham, Zendaoui, Fairouz, Hamami-Mitiche, Latifa. (1404). 'AI-Enhanced Intrusion Detection: Integrating Expert Knowledge and Machine Learning for Enterprise Networks', , 17(4), pp. 99-116. doi: 10.22059/jitm.2025.105485
Allam, Fatima Zohra, Bousbia-Salah, Hicham, Zendaoui, Fairouz, Hamami-Mitiche, Latifa. AI-Enhanced Intrusion Detection: Integrating Expert Knowledge and Machine Learning for Enterprise Networks. , 1404; 17(4): 99-116. doi: 10.22059/jitm.2025.105485

AI-Enhanced Intrusion Detection: Integrating Expert Knowledge and Machine Learning for Enterprise Networks

Journal of Information Technology Management
دوره 17، شماره 4، 2025، صفحه 99-116    اصل مقاله (1.32 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jitm.2025.105485
نویسندگان
Fatima Zohra Allam* 1؛ Hicham Bousbia-Salah2؛ Fairouz Zendaoui3؛ Latifa Hamami-Mitiche2
1Assistant Prof., Signal and Communication Laboratory, Department of Electronics, National Polytechnic School, Algeria
2Prof., Signal and Communication Laboratory, Department of Electronics, National Polytechnic School, Algeria.
3Assistant Prof., Laboratoire de la Communication dans les Systèmes Informatiques, Ecole Nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Alger, Algérie.
چکیده
Enterprise networks, as the backbone of modern information systems, are increasingly exposed to sophisticated and rapidly evolving cyber threats. Traditional Intrusion Detection Systems (IDS), based on static attack signatures, often fail to detect novel or complex intrusions, resulting in high false alarm rates. This study proposes an intelligent IDS that leverages Machine Learning and Deep Learning techniques to significantly improve detection accuracy and reduce alert noise. The system is capable of classifying attacks by severity and provides an intuitive interface to support efficient threat monitoring. Beyond technical performance, the solution addresses managerial objectives by lowering maintenance costs, enhancing service quality, accelerating incident response, and ensuring high availability with straightforward deployment. The proposed model offers a scalable and resilient IDS tailored for enterprise environments, contributing both practical and strategic value in the fight against increasingly sophisticated cyberattacks.
کلیدواژه‌ها
Intrusion Detection Systems؛ Artificial Intelligence؛ Deep Learning؛ Machine Learning
مراجع
آمار
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