SMART: a practical and robust client-side RAP detection approach

Rogue Access Points (RAPs) pose a persistent security threat to IEEE 802.11 Wireless Local Area Networks (WLANs). These attacks enable attackers to monitor, manipulate, and tamper with victims’ communications, resulting in significant privacy breaches and financial losses annually. Among the existing methods, client-based RAP detection has demonstrated greater effectiveness compared to admin-based approaches, primarily due to its wider applicability in real-world environments. However, existing series-model RAP detection methods often suffer from limited robustness and scope of application. To address these challenges, we propose SMART, an innovative series-model RAP detection method. SMART leverages special frame length sequences and arrival times as detection metrics, transforming the RAP detection problem into one of identifying malicious forwarding behavior. This is further simplified into the task of searching for specific sequences and analyzing their arrival times. By designing distinct frame length sequences and setting precise time windows, SMART effectively detects RAPs by identifying abnormal forwarding behavior. Extensive experiments demonstrate that SMART achieves a 100% detection rate in low and medium traffic scenarios and a 98.33% detection rate in high-traffic scenarios, performing reliably in both open and encrypted networks.