Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/46269
Title: Caracterização de tráfego de intrusões por meio de algoritmo de aprendizagem profunda
Authors: Zegarra Rodriguez, Demóstenes
Rosa, Renata Lopes
Silva, Katia Cilene Neles da
Gertrudes, Jadson Castro
Keywords: Sistema de detecção de intrusão em redes
Aprendizado de máquina
Aprendizado profundo
Network intrusion detection system
Machine learning
Deep learning
Issue Date: 14-May-2021
Publisher: Universidade Federal de Lavras
Citation: MENDONÇA, R. V. Caracterização de tráfego de intrusões por meio de algoritmo de aprendizagem profunda. 2021. 92 p. Tese (Doutorado em Ciência da Computação) – Universidade Federal de Lavras, Lavras, 2021.
Abstract: Machine learning algorithms, especially deep learning algorithms, are being applied in several areas of knowledge, such as image processing, video, voice, text and computer network traffic analysis. Computer networks and services offered to users in general have attracted the attention of attackers, generating a significant increase in potential damage to such services. To solve this problem, Intrusion Detection Systems are used to prevent attacks. However, there are still flaws in the detection, with a high false positive index. In this context, this work proposes a Deep Learning model, called Tree-CNN (SRS), which detects anomalous traffic, increasing the accuracy in the classification of DDoS attacks, Infiltration, Web and Brute force, cited as the main attacks on computer networks. For this, the results obtained with the proposed model were compared to the results obtained with the algorithms Naive Bayes, Random Forest, Decision Tree, Support Vector Machine, Multilayer Perceptron, Convolutional Neural Networks and Deep Belief Networks, where the proposed model obtained superior results in all scenarios, thus reducing the number of false positives in the classification of network traffic. In the captured traffic, some characteristics of the most common attacks were analyzed, using attribute selection techniques and Principal Component Analysis to reduce dimensionality.
URI: http://repositorio.ufla.br/jspui/handle/1/46269
Appears in Collections:Ciência da Computação - Mestrado (Dissertações)



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