Indoor WLAN Intrusion Detection Using Intra-class Transfer Learning with Low Effort

With the widespread adoption of Wireless Local Area Network (WLAN) in indoor environment, indoor WLAN intrusion detection has become a key technique in various fields with the advantage of accomplishing intrusion detection without any requirement of special device or collaboration from the target. However, this technique is suffered by a serious problem that the offline database construction normally leads to high manpower and time cost especially for the large-scale indoor environment. To address this problem, a new indoor WLAN intrusion detection approach with low effort is proposed in this paper. Specially, first of all, the difference between the Received Signal Strength (RSS) data in source and target domains at the same locations is reduced by intra-class transfer learning with the purpose of applying the relations between the offline RSS data and their labels to the online RSS data. Second, the RSS data in target domain are classified by using the classifier trained from the relations of RSS data and the corresponding labels in source domain. Third, the iterative transfer learning between source and target domains is conducted to obtain the labels of RSS data in target domain. Finally, the experimental results demonstrate that the proposed approach is able to achieve high detection accuracy as well as the strong robustness to the number of RSS data used for database construction.