WLAN Indoor Intrusion Detection Approach Based on Multiple Kernel Maximum Mean Discrepancy Transfer Learning
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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。
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.
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表 1 不同分类器的检测性能(%)
类别 FP FN DA KNN(迁移前) 35.92 0 75.60 KNN(迁移后) 0 0 99.78 RF(迁移前) 6.67 1.92 83.96 RF(迁移后) 0 0 98.90 SVM(迁移前) 18.02 0 93.85 SVM(迁移后) 0 1.10 98.02 
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