Citation

Arisandi, Diki (2026) Rogue Access Point identification via Beacon Anomaly detection. PhD thesis, Multimedia University.

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Abstract

Rogue Access Points (RAPs) pose a significant cybersecurity threat by imitating legitimate Wi-Fi networks and potentially compromising sensitive user data. To mitigate this issue, this study proposes a passive and client-centric detection mechanism based on a multi-module architecture for accurate RAP identification. The proposed mechanism comprises four primary modules: sniffer, detection, probing, and comparison, each designed to perform specific tasks within the detection process. The system was evaluated using a decision tree model across three different scenarios: controlled laboratory environments, real-world open environments, and deauthentication attack conditions. Multiple performance metrics were used to assess the effectiveness and reliability of the proposed mechanism under varying network conditions. Experimental results demonstrated consistently strong detection performance across all scenarios. In the controlled environment, the model obtained an Area Under the Curve (AUC) score of 0.921 and a Classification Accuracy (CA) of 0.875, indicating reliable differentiation between legitimate and rogue access points. In the open environment scenario, the performance further improved with an AUC score of 0.952 and a CA of 0.994. Under deauthentication attack conditions, the model achieved the highest performance, recording an AUC score of 0.955 and a CA of 0.996. Overall, the findings indicate that the proposed mechanism is effective, robust, and reliable for detecting rogue access points across diverse network environments and attack scenarios.

Item Type:	Thesis (PhD)
Additional Information:	Call No.: TK5105.59 .A75 2026
Uncontrolled Keywords:	Computer networks—Security measures
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	22 Jul 2026 08:23
Last Modified:	22 Jul 2026 08:23
URII:	http://shdl.mmu.edu.my/id/eprint/16376

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