Dynamic Anomaly Detection Using Vector Autoregressive Model
Identifying vandal users or attackers hidden in dynamic online social network data has been shown a challenging problem.In this work,we develop a dynamic attack/anomaly detection approach using a novel combination of the graph spectral features and the restricted Vector Autoregressive(rVAR)model.Our approach utilizes the time series modeling method on the non-randomness metric derived from the graph spectral features to capture the abnormal activities and interactions of individuals.Furthermore,we demonstrate how to utilize Granger causality test on the fitted rVAR model to identify causal relationships of user activities,which could be further translated to endogenous and/or exogenous influences for each individuals anomaly measures.We conduct empirical evaluations on the Wikipedia vandal detection dataset to demonstrate efficacy of our proposed approach.
Anomaly detection Vector autoregression Granger causality Dynamic graph Matrix perturbation Spectral graph analysis
Yuemeng Li Aidong Lu Xintao Wu Shuhan Yuan
University of North Carolina at Charlotte,Charlotte,USA University of Arkansas,Fayetteville,USA
国际会议
澳门
英文
600-611
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）