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arXiv · 1812.01941

Anomaly Detection for Network Connection Logs

Swapneel Mehta·Prasanth Kothuri·Daniel Lanza Garcia
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Abstract

We leverage a streaming architecture based on ELK, Spark and Hadoop in order to collect, store, and analyse database connection logs in near real-time. The proposed system investigates outliers using unsupervised learning; widely adopted clustering and classification algorithms for log data, highlighting the subtle variances in each model by visualisation of outliers. Arriving at a novel solution to evaluate untagged, unfiltered connection logs, we propose an approach that can be extrapolated to a generalised system of analysing connection logs across a large infrastructure comprising thousands of individual nodes and generating hundreds of lines in logs per second.

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BibTeXRIS

Swapneel Mehta, Prasanth Kothuri, Daniel Lanza Garcia. 2018-12-01. Anomaly Detection for Network Connection Logs. https://arxiv.org/abs/1812.01941

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