Anomaly Detection in Event-triggered Traffic Time Series via Similarity Learning

• URL: http://arxiv.org/abs/2506.16855v1
• Date: Fri, 20 Jun 2025 09:09:04 GMT
• Title: Anomaly Detection in Event-triggered Traffic Time Series via Similarity Learning
• Authors: Shaoyu Dou, Kai Yang, Yang Jiao, Chengbo Qiu, Kui Ren,
• Abstract summary: The overarching goal of this paper is to develop an unsupervised learning framework that is capable of learning similarities among a set of event-triggered time series.<n>The proposed framework aspires to offer a stepping stone that gives rise to a systematic approach to model and learn similarities among a multitude of event-triggered time series.
• Score: 16.352022450912013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series analysis has achieved great success in cyber security such as intrusion detection and device identification. Learning similarities among multiple time series is a crucial problem since it serves as the foundation for downstream analysis. Due to the complex temporal dynamics of the event-triggered time series, it often remains unclear which similarity metric is appropriate for security-related tasks, such as anomaly detection and clustering. The overarching goal of this paper is to develop an unsupervised learning framework that is capable of learning similarities among a set of event-triggered time series. From the machine learning vantage point, the proposed framework harnesses the power of both hierarchical multi-resolution sequential autoencoders and the Gaussian Mixture Model (GMM) to effectively learn the low-dimensional representations from the time series. Finally, the obtained similarity measure can be easily visualized for the explanation. The proposed framework aspires to offer a stepping stone that gives rise to a systematic approach to model and learn similarities among a multitude of event-triggered time series. Through extensive qualitative and quantitative experiments, it is revealed that the proposed method outperforms state-of-the-art methods considerably.

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