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Indoor intrusion detection based on deep signal feature fusion and minimized-MKMMD transfer learning
Physical Communication ( IF 2.2 ) Pub Date : 2020-07-06 , DOI: 10.1016/j.phycom.2020.101164
Mu Zhou , Xinyue Li , Yong Wang , Yaoping Li , Aihu Ren

Indoor intrusion detection based on Wireless Local Area Network (WLAN) has been widely used for security monitoring, smart homes, entertainment games, and many other fields in the Beyond 5G (B5G) wireless communication network environment. Most existing indoor intrusion detection methods directly exploit the Received Signal Strength (RSS) data collected by Monitor Points (MPs) and pay little attention to the instability of WLAN signals in complex indoor environments. In response to this problem, this paper proposes a novel WLAN indoor intrusion detection method based on deep signal feature fusion and Minimized Multiple Kernel Maximum Mean Discrepancy (Minimized-MKMMD). Firstly, the multi-branch deep convolutional neural network is used to conduct the dimensionality reduction and feature fusion of the RSS data, and the tags are obtained according to the features of the offline and online RSS fusion features that are corresponding to the silence and intrusion states, and then based on this, the source domain and target domain are constructed respectively. Secondly, the optimal transfer matrix is constructed by minimizing MKMMD. Thirdly, the transferred RSS data in the source domain is utilized for training the classifiers that are applying in getting the classification of the RSS fusion features in the target domain in the same shared subspace. Finally, the intrusion detection of the target environment is realized by iteratively updating the process above until the algorithm converges. The experimental results show that the proposed method can effectively improve the accuracy and robustness of the intrusion detection system.



中文翻译:

基于深度信号特征融合和最小化MKMMD转移学习的室内入侵检测

基于无线局域网(WLAN)的室内入侵检测已广泛用于安全监控,智能家居,娱乐游戏以及Beyond 5G(B5G)无线通信网络环境中的许多其他领域。大多数现有的室内入侵检测方法都直接利用监视点(MP)收集的接收信号强度(RSS)数据,而很少注意复杂室内环境中WLAN信号的不稳定性。针对这一问题,本文提出了一种基于深度信号特征融合和最小多核最大均值误差(Minimized-MKMMD)的WLAN室内入侵检测方法。首先,使用多分支深度卷积神经网络对RSS数据进行降维和特征融合,根据沉默和入侵状态对应的离线和在线RSS融合特征,分别得到标签,然后在此基础上分别构造源域和目标域。其次,通过最小化MKMMD来构造最优传递矩阵。第三,在源域中传输的RSS数据用于训练分类器,这些分类器用于在同一共享子空间中获取目标域中RSS融合功能的分类。最后,通过迭代更新上述过程直到算法收敛,实现了目标环境的入侵检测。实验结果表明,该方法可以有效提高入侵检测系统的准确性和鲁棒性。

更新日期:2020-07-06
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