Hdl Handle:
http://hdl.handle.net/11285/572110
Title:
Low Overhead Host-Based IDS
Authors:
Aguilar Rodríguez, Ignacio J.
Issue Date:
01/07/2004
Abstract:
The area of Intrusion Detection is very important these days. Companies have acquired more interest in having this type of systems beacuse of the importance that information has for them. Machine learning algorithms are being used along with IDSs as an efficient approach. For these reasons we work with this approach in this thesis, presenting from general to specific, the information of the models and types of IDSs, and some machine learning algorithms and some fusion rules for them, that can help achieving a good IDS. In this work, we focus on Host-based intrusion detection, and three machine learning algorithms, which are C4.5, RIPPER and PART. It is showed a method to reduce false alarm rates and with this, increasing the possibility of detecting true alarms when our system trigger them.
Keywords:
IDS; Host-based IDS; Low Overhead IDS; Telecommunications; Electronic Engineering
Advisors:
Jorge Carlos Max Perera
Committee Member / Sinodal:
José Ramón Rodríguez; Artemio Aguilar Coutiño
Degree Level:
Master of Science in Electronic Engineering Major in Telecommunications
School:
Electrónica, Computación, Información y Comunicaciones
Campus Program:
Campus Monterrey
Discipline:
Ingeniería y Ciencias Aplicadas / Engineering & Applied Sciences
Appears in Collections:
Ciencias Exactas

Full metadata record

DC FieldValue Language
dc.contributor.advisorJorge Carlos Max Pereraes
dc.contributor.authorAguilar Rodríguez, Ignacio J.en
dc.date.accessioned2015-08-17T11:21:19Zen
dc.date.available2015-08-17T11:21:19Zen
dc.date.issued01/07/2004-
dc.identifier.urihttp://hdl.handle.net/11285/572110en
dc.description.abstractThe area of Intrusion Detection is very important these days. Companies have acquired more interest in having this type of systems beacuse of the importance that information has for them. Machine learning algorithms are being used along with IDSs as an efficient approach. For these reasons we work with this approach in this thesis, presenting from general to specific, the information of the models and types of IDSs, and some machine learning algorithms and some fusion rules for them, that can help achieving a good IDS. In this work, we focus on Host-based intrusion detection, and three machine learning algorithms, which are C4.5, RIPPER and PART. It is showed a method to reduce false alarm rates and with this, increasing the possibility of detecting true alarms when our system trigger them.es
dc.language.isoen-
dc.rightsOpen Accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleLow Overhead Host-Based IDSen
dc.typeTesis de Maestríaes
dc.contributor.departmentITESMen
thesis.degree.grantorInstituto Tecnológico y de Estudios Superiores de Monterreyes
thesis.degree.levelMaster of Science in Electronic Engineering Major in Telecommunicationsen
dc.contributor.committeememberJosé Ramón Rodríguezes
dc.contributor.committeememberArtemio Aguilar Coutiñoes
thesis.degree.disciplineElectrónica, Computación, Información y Comunicacioneses
dc.subject.keywordIDSes
dc.subject.keywordHost-based IDSes
dc.subject.keywordLow Overhead IDSes
dc.subject.keywordTelecommunicationses
dc.subject.keywordElectronic Engineeringes
thesis.degree.programCampus Monterreyes
dc.subject.disciplineIngeniería y Ciencias Aplicadas / Engineering & Applied Sciencesen
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