Anomalous Event Sequence Detection

Boxiang Dong

doi:10.1109/MIS.2020.3041174

Anomalous Event Sequence Detection

2021-05-01

5

PRE

AI

DOI：10.1109/MIS.2020.3041174

Original

Original request for help

Save

Abstract

En 中文

Anomaly detection has been widely applied in modern data-driven security applications to detect abnormal events/entities that deviate from the majority. However, less work has been done in terms of detecting suspicious event sequences/paths, which are better discriminators than single events/entities for distinguishing normal and abnormal behaviors in complex systems such as cyber-physical systems. A key and challenging step in this endeavor is how to discover those abnormal event sequences from millions of system event records in an efficient and accurate way. To address this issue, we propose NINA, a network diffusion based algorithm for identifying anomalous event sequences. Experimental results on both static and streaming data show that NINA is efficient (processes about 2 million records per minute) and accurate.

Keywords:

Convergence

Receivers

Anomaly detection

Surveillance

Mathematical model

Intelligent systems

Complex systems

anomaly detection

intrusion detection

graph mining

sequence discovery

Anomalous Event Sequence Detection

Abstract

AI Summary

Journal

Organization