An Adaptive Passive Radio Map Construction for Indoor WLAN Intrusion Detection

Abstract

Indoor WLAN intrusion detection technique for the anonymous target has been widely applied in many fields such as the smart home management, security monitoring, counterterrorism, and disaster relief. However, the existing indoor WLAN intrusion detection systems usually require constructing a passive radio map involving a lot of manpower and time cost, which is a significant barrier of the deployment of WLAN intrusion detection systems. In this paper, we propose to use the adaptive-depth ray tree model to automatically construct an adaptive passive radio map for indoor WLAN intrusion detection. In concrete terms, the quasi-3D ray-tracing model is enhanced by using the genetic algorithm to predict the received signal strength (RSS) propagation feature under the indoor silence and intrusion scenarios, which improves the computational efficiency while preserving the accuracy of passive radio map. Then, the RSS mean, variance, maximum, minimum, range, and median are allied to increase the robustness of passive radio map. Finally, we conduct empirical evaluations on the real-world data to validate the high intrusion detection rate and low database construction cost of the proposed method.