周牧¹,
李垚鲆^1,,,
谢良波¹,
蒲巧林²,
田增山¹
1.重庆邮电大学通信与信息工程学院 重庆 400065
2.香港浸会大学计算机系 香港 999077
基金项目:国家自然科学基金(61771083)，重庆市基础与前沿研究计划基金(cstc2017jcyjAX0380)，重庆市研究生科研创新项目(CYS18240)

详细信息

作者简介:周牧：男，1984年生，教授，博士生导师，主要研究方向为无线定位与导航技术、信号处理与检测技术、机器学习与信息融合技术等
李垚鲆：女，1995年生，硕士生，研究方向为室内入侵检测技术
谢良波：男，1986年生，副教授，主要研究方向为射频识别技术、室内定位技术等
蒲巧林：女，1988年生，助教，主要研究方向为机器学习、室内定位技术等
田增山：男，1968年生，教授，博士生导师，主要研究方向为移动通信、个人通信、GPS及蜂窝网定位技术等

通讯作者:李垚鲆　liyaopingna@foxmail.com

中图分类号:TN911.23

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出版历程

收稿日期:2019-05-21
修回日期:2019-11-27
网络出版日期:2019-12-18
刊出日期:2020-06-04

WLAN Indoor Intrusion Detection Approach Based on Multiple Kernel Maximum Mean Discrepancy Transfer Learning

Mu ZHOU¹,
Yaoping LI^1,,,
Liangbo XIE¹,
Qiaolin PU²,
Zengshan TIAN¹
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Department of Computer Science, Hong Kong Baptist University, Hong Kong 999077, China
Funds:The National Natural Science Foundation of China (61771083), The Fundamental and Frontier Research Project of Chongqing (cstc2017jcyjAX0380), The Postgraduate Scientific Research and Innovation Project of Chongqing (CYS18240)


摘要
摘要:无线局域网(WLAN)室内入侵检测技术是目前智能检测领域的研究热点之一，而传统基于数据库构建的入侵检测技术没有考虑复杂室内环境中WLAN信号的时变性，从而导致WLAN室内入侵检测系统的鲁棒性较差。为了解决这一问题，该文提出一种基于多核最大均值差异(MKMMD)迁移学习的WLAN室内入侵检测方法。该方法首先利用离线有标记和在线伪标记的接收信号强度(RSS)特征来分别构建源域和目标域；其次，通过构造最优迁移矩阵以最小化源域和目标域RSS特征混合分布之间的MKMMD；再次，利用迁移后的源域RSS特征与对应标签来训练分类器，并将其用于对迁移后的目标域RSS特征进行分类以得到目标域标签集；最后，迭代更新目标域标签集直至算法收敛，进而实现对目标环境的入侵检测。实验结果表明，该文所提方法在保证较高检测精度的同时，能够有效克服信号时变性对检测性能的影响。
关键词:室内入侵检测/
多核最大均值差异/
迁移学习/
最优迁移矩阵/
无线局域网
Abstract:Wireless Local Area Network (WLAN) indoor intrusion detection technique is one of the current research hotspots in the field of intelligent detection, but the conventional database construction based intrusion detection technique does not consider the time-variant property of WLAN signal in the complicated indoor environment, which results in the low robustness of WLAN indoor intrusion detection system. To address this problem, a Multiple Kernel Maximum Mean Discrepancy (MKMMD) transfer learning based WLAN indoor intrusion detection approach is proposed. First of all, the offline labeled and online pseudo-labeled Received Signal Strength (RSS) features are used to construct source and target domains respectively. Second, the optimal transfer matrix is constructed to minimize the MKMMD of the joint distributions of RSS features in source and target domains. Third, a classifier trained from the transferred RSS features and the corresponding labels in source domain is used to classify the transferred RSS features in target domain, and meanwhile the label set corresponding to target domain is obtained. Finally, the label set corresponding to target domain is updated in an iterative manner until the proposed algorithm converges, and then the intrusion detection in target environment is achieved. The experimental results indicate that the proposed approach is able to preserve high detection accuracy as well as overcome the impact of time-variant signal property on the detection performance.
Key words:Indoor intrusion detection/
Multiple Kernel Maximum Mean Discrepancy (MKMMD)/
Transfer learning/
Optimal transfer matrix/
Wireless Local Area Network (WLAN)



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