Abstract
The existing passive intrusion detection technology has poor adaptability under different monitoring environments and low detection performance, this paper proposes a wireless local area network (WLAN) indoor passive intrusion detection method based on Support Vector Domain Description (SVDD). A-distance is adopted to evaluate multiple features to correctly distinguish the average contribution of the two states of silence and intrusion, screening the extreme difference and variance as the characteristic quantity of the signal change. Then, the paper introduces the single classification method SVDD to train the hypersphere anomaly detection boundary in the high dimensional feature space. We can achieve accurate anomaly detection by determining whether the current sample point is within the hypersphere. In a typical indoor environment, compared with the existing detection algorithms, the proposed method achieves better detection performance under low overhead conditions. F1-measure which is the system evaluation index increased by nearly 4%.
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