Suspicious Network Event Recognition Leveraging on Machine Learning

Autor:	Maurizio Martinelli, Daniele Sartiano, Luca Deri, Giuseppe Attardi
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	business.industry Event (computing) Computer science Event recognition Big data network events Machine learning computer.software_genre gradient boosting cyber-security machine learning Software deployment Gradient boosting Artificial intelligence business computer
Zdroj:	IEEE BigData IEEE BigData 2019, pp. 5915–5920, Los Angeles, 09-12/12/2019 info:cnr-pdr/source/autori:Sartiano D. (1); Attardi G. (2); Deri L. (1); Martinelli M (1)/congresso_nome:IEEE BigData 2019/congresso_luogo:Los Angeles/congresso_data:09-12%2F12%2F2019/anno:2019/pagina_da:5915/pagina_a:5920/intervallo_pagine:5915–5920
Popis:	Network log events produced by network probes are used by security analyzers to detect traffic anomalies and threats. While it is relatively trivial for a probe to mark specific events as suspicious, it is much more challenging for log analyzers to create a comprehensive picture of the overall network. Machine learning can potentially help in this, however there is no specific solution for analyzing network event logs. This paper covers the experiments and design choices that have been made to create a machine learning-based tool able to analyze network event logs. The tool has been evaluated in the Suspicious Network Event Recognition Cup at IEEE BigData 2019, achieving an AUC (Area Under the Curve) of over 90%, making it accurate enough for deployment in real life scenarios.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1e2edce3eec059c0687493cad3125e6 http://hdl.handle.net/11568/1013724 Zobrazit plný text záznamu