A novel physical layer spoofing detection based on sparse signal processing

In wireless communication systems, spoofing attack significantly impact the information security, for this attack is not hard to launch with little effort. Although traditional cryptographic authentication can be utilized for node identification, it is not desirable in some low power requirements application scenarios such as Wireless Sensor Networks (WSNs). In this paper, we formulate this problem as one of sparse signal processing. In order to identify the existence of the spoofing attacker, we explore using the feature extraction and fusion to establish an automatic representative selection algorithm. By examining the correlation between the two selected targets, we can determine the attacking situation. We evaluate our scheme through an experimental system, and the experimental results on real measured data show that our sparse signal processing can reach desired identification performance.