Anomaly Rule Detection in Sequence Data

TL;DR

This paper introduces DUOS, a novel framework for anomaly detection in sequence data that considers both the anomalousness and utility of patterns, improving effectiveness and efficiency over existing methods.

Contribution

The paper proposes a new utility-aware outlier sequential rule framework and efficient pruning strategies, advancing anomaly detection in sequence data beyond frequency-based approaches.

Findings

DUOS outperforms baseline algorithms in effectiveness.

DUOS demonstrates superior efficiency and scalability.

Experimental results on real-world datasets validate the approach.

Abstract

Analyzing sequence data usually leads to the discovery of interesting patterns and then anomaly detection. In recent years, numerous frameworks and methods have been proposed to discover interesting patterns in sequence data as well as detect anomalous behavior. However, existing algorithms mainly focus on frequency-driven analytic, and they are challenging to be applied in real-world settings. In this work, we present a new anomaly detection framework called DUOS that enables Discovery of Utility-aware Outlier Sequential rules from a set of sequences. In this pattern-based anomaly detection algorithm, we incorporate both the anomalousness and utility of a group, and then introduce the concept of utility-aware outlier sequential rule (UOSR). We show that this is a more meaningful way for detecting anomalies. Besides, we propose some efficient pruning strategies w.r.t. upper bounds for mining UOSR, as well as the outlier detection. An extensive experimental study conducted on several real-world datasets shows that the proposed DUOS algorithm has a better effectiveness and efficiency. Finally, DUOS outperforms the baseline algorithm and has a suitable scalability.