@INBOOK{Lemieszewski2025:i8600,
	author="{Lemieszewski, Łukasz and Remiszewski, Grzegorz and Borkowski, Piotr and Wolak, Aleksander and Szymczyk, Janusz and Becker, Jarosław}",
	title="{Anomaly-Based Detection of Rogue Access Points in High-Risk Network Infrastructures}",
	year="2025",
	type="rozdział w monografii naukowej / referat",
	language="en",
	booktitle="{Emerging Challenges in Intelligent Management Information Systems : Proceedings of 28th European Conference on Artificial Intelligence ECAI 2025 - IMIS Workshop, Volume 2}",
	publisher="{Springer}",
	series="{Lecture Notes in Networks and Systems}",
	pages="15--28",
	doi="10.1007/978-3-032-06611-4_2",
	url="https://link.springer.com/chapter/10.1007/978-3-032-06611-4_2",
	keywords="Wi-Fi anomaly detection; machine learning; Rogue AP",
	abstract="{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.}",
}