Rogue Access Point Localization Leveraging Compressive Sensing via Kernel Optimization

With the pervasive infrastructures of WLAN, user's privacy has emerged as an important security and privacy problem. Rogue Access Points (AP), as one of the threat, is expected to be detected and located accurately. Therefore, in this paper, we propose a novel rogue AP localization method leveraging compressive sensing (CS) via kernel optimization. Although the CS based technique has been widely used in mobile user localization system, this is the first time to apply it to reversely localize AP. In addition, designing an appropriate kernel is the key to successful application of CS technique, however, traditional Gaussian or Bernoulli random kernels could not be utilized in rogue AP localization system, due that the kernel is related to the number and distribution of monitors, which could not randomly change every time. Hence, for CS kernel optimization, we firstly deduce the minimum number of monitors required in this system through a theoretical analysis which aims at justifying the validity of problem formulation. Then an Equiangular Tight Frame (ETF) based monitors distribution scheme is presented to achieve higher location accuracy. Finally, we perform both simulations and experiments to demonstrate the superiority of our approach as compare to other methods theoretically and practically.