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Abnormal user behavior detection algorithm based on dynamic time span and cluster difference index
Zhan Lin1,，Zeng Xianhui1,，2，Dai Kaixuan1
1.College of Information Science and Technology,，Donghua University,，Shanghai 201620，China,； 2.Engineering Research Center of Digitized Textile &Apparel Technology,，Ministry of Education，Shanghai 201620,，China)
Abstract： In order to analyze the behavior of residents under the condition of ensuring real-time performance and adaptability of the model, this paper proposes a real-time detection algorithm for abnormal user behavior based on dynamic time span and clustering difference index. The algorithm uses dynamic time span and cluster difference index to detect concept drift in real-time data streams, and uses local outlier factor(LOF) to detect the time points when users have abnormal behaviors when concept drift occurs in data streams. The classification model is continuously updated through the dynamic time span, which effectively improves the applicability of the model. Experimental results show that the algorithm can correctly detect concept drift while ensuring real-time performance, and give the time point when user behavior is abnormal. The research results of this paper provide new ideas for the realization of abnormal user behavior detection in the smart home environment, and can provide effective services and security guarantees for home people.
Key words : smart home,；clustering algorithm；clustering difference index,；LOF index
