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dc.contributor.advisorSotelo Monge, Marco Antonio
dc.contributor.authorHuancayo Ramos, Katherinne Shirley
dc.date.accessioned2021-03-17T12:35:05Z
dc.date.available2021-03-17T12:35:05Z
dc.date.issued2020
dc.identifier.citationHuancayo Ramos, K. S. (2020). Benchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analytics [Tesis para optar el Título Profesional de Ingeniero de Sistemas, Universidad de Lima]. Repositorio institucional de la Universidad de Lima. https://hdl.handle.net/20.500.12724/12724es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12724/12724
dc.description.abstractBotnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns.es_EN
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherUniversidad de Limaes_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.sourceRepositorio Institucional - Ulimaes_PE
dc.sourceUniversidad de Limaes_PE
dc.subjectBotnetses_PE
dc.subjectMalware (Programas de computadora)es_PE
dc.subjectSeguridad informática
dc.subjectMalware (Computer software)
dc.subjectComputer security
dc.subject.classificationIngeniería de sistemas / Softwarees_PE
dc.titleBenchmark-Based Reference Model for Evaluating Botnet Detection Tools Driven by Traffic-Flow Analyticses_PE
dc.typeinfo:eu-repo/semantics/bachelorThesises_PE
thesis.degree.disciplineIngeniería de sistemases_PE
thesis.degree.grantorUniversidad de Lima. Facultad de Ingeniería y Arquitecturaes_PE
thesis.degree.levelTítulo profesionales_PE
thesis.degree.nameIngeniero de sistemases_PE
dc.publisher.countryPerúes_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE
renati.author.dni74635102
renati.advisor.orcidhttps://orcid.org/0000-0001-6392-0216es_PE
renati.advisor.dni41587313
renati.jurorRodriguez-Rodriguez, Nadia-Katherine
renati.jurorGutierrez-Cardenas, Juan-Manuel
renati.jurorNina-Hanco, Hernan
renati.levelhttp://purl.org/pe-repo/renati/level#tituloProfesionales_PE
renati.typehttp://purl.org/pe-repo/renati/type#tesises_PE
renati.discipline612076es_PE


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