2025

rozdział w monografii naukowej / referat

angielski

EN This article presents a comprehensive and scalable system for the detection and mitigation of Rogue Access Points (APs) in Wi-Fi networks, addressing a significant security risk in distributed infrastructures. The proposed solution integrates multiple analytical layers, including passive and active traffic analysis, anomaly detection (Isolation Forest, One-Class SVM), supervised machine learning (Random Forest, XGBoost), and explainable artificial intelligence (XAI) mechanisms. A local whitelist is used for initial verification, with unknown devices triggering advanced analysis. Detected threats generate alerts via Snort and are logged and visualized in real time using OpenSearch and a lightweight browser plugin. Unlike previous approaches, this system combines detection with automated response, real-time stream analytics, and interpretable decision-making within a unified architecture. Experimental results in real-world conditions demonstrate over 95% detection accuracy and strong resilience to false positives. The solution shows high potential for deployment in high-risk, dynamic wireless network environments.

12.10.2025

15 - 28

10.1007/978-3-032-06611-4_2

https://link.springer.com/chapter/10.1007/978-3-032-06611-4_2

Emerging Challenges in Intelligent Management Information Systems : Proceedings of 28th European Conference on Artificial Intelligence ECAI 2025 - IMIS Workshop, Volume 2

28th European Conference on Artificial Intelligence ECAI 2025, 25-30.10.2025, Bologna, Włochy

20

140