000			01892naa a2200241 a 4500
003			AR-LpUFIB
005			20250311171200.0
008			230201s2019 xx o 000 0 eng d
024	8		_aDIF-M8371 _b8591 _zDIF007663
040			_aAR-LpUFIB _bspa _cAR-LpUFIB
100	1		_aVenosa, Paula
245	1	0	_aEnsembling to improve infected hosts detection
300			_a1 archivo (824,2 kB)
500			_aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
520			_aIn 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, 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.
534			_aCongreso Argentino de Ciencias de la Computación (25to : 2019 : Río Cuarto, Córdoba)
650		4	_aSEGURIDAD INFORMÁTICA
653			_adetección de intrusos
700	1		_aGarcía, Sebastián
700	1		_aDíaz, Francisco Javier _94623
856	4	0	_uhttp://sedici.unlp.edu.ar/handle/10915/90565
942			_cCP
999			_c57436 _d57436