Ensembling to improve infected hosts detection

In this paper we describe the main ensemble learning techniques and their application in the cybersecurity threats detection. The state of the art in the use of ensemble learning techniques is presented here as an alternative to the current intrusion detection mechanisms, analyzing their advantages...

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Detalles Bibliográficos
Autores principales:	Venosa, Paula, García, Sebastián, Díaz, Francisco Javier
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2019
Materias:	Ciencias Informáticas Ensemble leaming Cybersecurity Malware / spyware crime Intrusion detection systems
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/90565
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-90565
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas Ensemble leaming Cybersecurity Malware / spyware crime Intrusion detection systems
spellingShingle	Ciencias Informáticas Ensemble leaming Cybersecurity Malware / spyware crime Intrusion detection systems Venosa, Paula García, Sebastián Díaz, Francisco Javier Ensembling to improve infected hosts detection
topic_facet	Ciencias Informáticas Ensemble leaming Cybersecurity Malware / spyware crime Intrusion detection systems
description	In this paper we describe the main ensemble learning techniques and their application in the cybersecurity threats detection. The state of the art in the use of ensemble learning techniques is presented here as an alternative to the current intrusion detection mechanisms, analyzing their advantages and disadvantages. We propose to incorporate ensemble learning to SLIPS [3], a behavioral-based intrusion detection and prevention system that uses machine learning algorithms to detect malicious behaviors, to obtain better results, taking advantage of the benefits of the SLIPS classifiers and modules. As part of this work we extend ensembling by considering algorithms from different domains (not machine learning domains), as Thread Intelligence. As a first stage of this project, performance tests of ensemble learning algorithms were performed to detect malware from flows evaluating its accuracy. The results of these tests are presented here, as well as the conclusions obtained and the future work.
format	Objeto de conferencia Objeto de conferencia
author	Venosa, Paula García, Sebastián Díaz, Francisco Javier
author_facet	Venosa, Paula García, Sebastián Díaz, Francisco Javier
author_sort	Venosa, Paula
title	Ensembling to improve infected hosts detection
title_short	Ensembling to improve infected hosts detection
title_full	Ensembling to improve infected hosts detection
title_fullStr	Ensembling to improve infected hosts detection
title_full_unstemmed	Ensembling to improve infected hosts detection
title_sort	ensembling to improve infected hosts detection
publishDate	2019
url	http://sedici.unlp.edu.ar/handle/10915/90565
work_keys_str_mv	AT venosapaula ensemblingtoimproveinfectedhostsdetection AT garciasebastian ensemblingtoimproveinfectedhostsdetection AT diazfranciscojavier ensemblingtoimproveinfectedhostsdetection
bdutipo_str	Repositorios
_version_	1764820490150477826

Ensembling to improve infected hosts detection

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