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24 April 2024 |
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Article overview
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Anomaly Detection for Network Connection Logs | | Swapneel Mehta
; Prasanth Kothuri
; Daniel Lanza Garcia
; | | Date: |
1 Dec 2018 | | 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. | | Source: | arXiv, 1812.1941 | | Services: | Forum | Review | PDF | Favorites | |
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