Analysis of RSS Patterns to Detect Rogue Access Points

The possibility for attacks that target the security and protection of data within the network has increased due to the increasing use of wireless LANs. Access points play a critical role in the growth of wireless local networks by providing users with benefits in interacting with the network and detecting attackers. This feature enables users who are not permitted to access this network's information and data to locate unauthorized (rogue) access points, which can be used for illegal reasons. This paper proposes an algorithm based on a set of features collected by legitimate access points using a Wi-Fi analysis app installed on the mobile phone, depending on the behavior of legitimate access points and using machine learning mechanisms, specifically the KNN model. The characteristics of each access point and their predicted patterns of behavior influence the proposed algorithm. The machine learning algorithm's total accuracy is 94.9%.