Daniel Vajda, Adrian Pekar, and Karoly Farkas

Towards Machine Learning-based Anomaly Detection on Time-Series Data

The complexity of network infrastructures is exponentially growing. Real-time monitoring of these infrastructures is essential to secure their reliable operation. The concept of telemetry has been introduced in recent years to foster this process by streaming time-series data that contain feature-rich information concerning the state of network components. In this paper, we focus on a particular application of telemetry — anomaly detection on time-series data. We rigorously examined state-of-the-art anomaly detection methods. Upon close inspection of the methods, we observed that none of them suits our requirements as they typically face several limitations when applied on time-series data. This paper presents Alter-Re2, an improved version of ReRe, a state-of-the-art Long Short- Term Memory-based machine learning algorithm. Throughout a systematic examination, we demonstrate that by introducing the concepts of ageing and sliding window, the major limitations of ReRe can be overcome. We assessed the efficacy of Alter-Re2 using ten different datasets and achieved promising results. Alter-Re2 performs three times better on average when compared to ReRe.

Reference:

DOI: 10.36244/ICJ.2021.1.5

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Please cite this paper the following way:

Daniel Vajda, Adrian Pekar and Karoly Farkas, "Towards Machine Learning-based Anomaly Detection on Time-Series Data", Infocommunications Journal, Vol. XIII, No 1, March 2021, pp. 36-44.