Maximum Mean Discrepancy Minimization Based Transfer Learning for Indoor WLAN Personnel Intrusion Detection

Abstract

Indoor personnel intrusion detection has been recognized as an active research topic over the last decade due to the remarkably growing demand for indoor security management, elderly monitoring, and smart home. In this circumstance, indoor wireless local area network (WLAN) personnel intrusion detection is one of the most promising approaches by considering its advantages of the handy accessibility of WLAN signal and convenient use of WLAN devices. Many existing studies rely on only the offline WLAN received signal strength (RSS) data to train a heuristic model, which is, then, used for online intrusion detection without considering the time-variant RSS property in the actual indoor environment. To address this problem, we propose a new maximum mean discrepancy (MMD) minimization based transfer learning approach for indoor WLAN personnel intrusion detection. Specifically, first of all, the source and target domains are constructed from offline labeled and online unlabeled RSS data, respectively. Second, the MMD of marginal distributions of the RSS data in source and target domains are calculated as the difference of these two domains. Third, the optimal transfer matrix corresponding to the minimum difference of source and target domains (or called minimum MMD) is constructed to transfer the RSS data in these two domains into the data in a same subspace. Finally, the classifiers used for intrusion detection are trained from these data with the purpose of enhancing the robustness of the proposed approach.