Indoor WLAN Personnel Intrusion Detection Using Transfer Learning-Aided Generative Adversarial Network with Light-Loaded
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Indoor WLAN Personnel Intrusion Detection Using Transfer Learning-Aided Generative Adversarial Network with Light-Loaded Database Mu Zhou 1 & Yaoping Li 1 & Hui Yuan 1 & Jiacheng Wang 1 & Qiaolin Pu 2 Accepted: 23 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The Internet of Everything (IoE) provides a platform that allows devices to be remotely connected, sensed, and controlled across the network infrastructure. The smart home in the era of the IoE is born on the basis of the high integration of emerging communication technologies such as big data, sensors, and machine learning. In this paper, we focus on wireless detection technologies using smartphones and computers in smart homes. Among them, the indoor Wireless Local Area Network (WLAN) personnel intrusion detection technology based on the database construction has become one of the comprehensive detection technologies by advantages of the convenient accessibility of the WLAN signal and minimal hardware requirement. However, the considerable labor and time cost involved in the database construction affects the popularity and application of database-based intrusion detection systems. To cope with this problem, we propose a new indoor WLAN personnel intrusion detection approach with the reduced overhead of the database construction. Specifically, first of all, the offline database is extended by fake Received Signal Strength (RSS) data, which are generated by the Generative Adversarial Network (GAN) based supervised learning from actual labeled RSS data. Second, the difference between the extended database and online RSS data caused by the time-variant environment noise is reduced by minimizing the Maximum Mean Discrepancy (MMD) between marginal distributions of RSS data through the transfer learning. Finally, the intrusion detection is achieved by classifying online RSS data with classifiers trained from the extended database. Furthermore, experimental results show that the proposed approach can not only perform well in reducing the database overhead and the difference of data in source and target domains, which are corresponding to the same environment state but also detect environment states with satisfactory accuracy. Keywords Personnel intrusion detection . Database extension . Generative adversarial network . Transfer learning . Wireless local area network
Abbreviations APs Access Points CDF Cumulative Density Function CSI Channel State Information DA Detection Accuracy FN False Negative FP False Positive
* Mu Zhou [email protected] 1
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, No. 2, Chongwen Road, Chongqing 400065, Nan’an District, China
2
Hong Kong Baptist University, Hong Kong 999077, China
GAN GPR HMM KNN LBSs LOS MA MMD MPs MV NIC PNN PRNN RF RFID RKHS
Generative Adversarial Network Gaussian Process Regression Hidden Markov Model K-Nearest Neighbor Location-based Services Line-of-sight Moving Average Maximum Mean Discrepancy Mobile
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