Integrated Redundant APs Reduction and Transfer Learning for Indoor WLAN Intrusion Detection via Link-Layer Data Transformation

Abstract

Indoor intrusion detection technology has been widely used in smart home management, public safety, disaster relief, and other fields. In recent years, with the rapid deployment of Wireless Local Area Network (WLAN) and general support of the IEEE 802.11 protocol by mobile devices, indoor intrusion detection can be realized conveniently. Most of the existing indoor intrusion detection algorithms have large computational and storage overheads and do not consider the instability of signals in the indoor environment. In response to this compelling problem, this paper proposes a new integrated redundant Access Points (APs) reduction and transfer learning for indoor WLAN intrusion detection via link-layer data transformation. First, the detection technology for mobile APs based on a fuzzy rough set is exploited to filter the redundant APs in the indoor environment. Second, the target domain and the source domain are constructed through the link-layer data of the online phase and the offline phase. Then, the Maximum Mean Deviation (MMD) minimum value corresponding to the two domains is worked out by the mathematical statistics method to obtain the optimized migration matrix, and the link-layer information of the two domains is transferred into the same subspace by using the matrix. Finally, the optimal intrusion detection classifiers are obtained by training the transferred link-layer data. This method not only has better robustness in the complex indoor environment but also reduces time and labor costs.