Anomaly detection in dynamic networks

2022/10/13 by Sevvandi Kandanaarachchi, Rob J. Hyndman, Kandanaarachchi, Sevvandi +1
Computer Science · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Complex Network Analysis Techniques #FOS: Computer and information sciences #Machine Learning (stat.ML) #Network Security and Intrusion Detection #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.2210.07407

openalex publication_date 2022/10/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Detecting anomalies from a series of temporal networks has many applications, including road accidents in transport networks and suspicious events in social networks. While there are many methods for network anomaly detection, statistical methods are under utilised in this space even though they have a long history and proven capability in handling temporal dependencies. In this paper, we introduce oddnet, a feature-based network anomaly detection method that uses time series methods to model temporal dependencies. We demonstrate the effectiveness of oddnet on synthetic and real-world datasets. The R package oddnet implements this algorithm.

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