中圖分類(lèi)號(hào)： TP301.6
文獻(xiàn)標(biāo)識(shí)碼： A
DOI： 10.19358/j.issn.2096-5133.2022.04.014
引用格式： 詹麟，曾獻(xiàn)輝，代凱旋. 基于動(dòng)態(tài)時(shí)間跨度與聚類(lèi)差異指數(shù)的用戶(hù)行為異常檢測(cè)算法[J].信息技術(shù)與網(wǎng)絡(luò)安全，2022，41(4)：90-96.

Abnormal user behavior detection algorithm based on dynamic time span and cluster difference index

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