Mitigating the Effect of the Rogue Access Point Based on Machine Learning

The widespread adoption of Wireless Local Area Network (WLAN) has boosted the demand for wireless devices that connect to the network without the use of cables. As a result, it became necessary to install WLAN infrastructure everywhere. The Access Point (AP) is a critical component of the WLAN infrastructure, allowing end-users to effortlessly connect to the Internet. WLAN, on the other hand, has various security difficulties, such as the introduction of a rogue AP (RAP), which is an unauthorized AP established and installed by the attacker in the WLAN, with the goal of stealing data and damaging the user's devices. In this paper, a machine learning-based approach for detecting and blocking RAPs is proposed. Experimental results shows that the proposed method is promising and can detect RAPs with reasonable accuracy.