Intrusion Detection System Based on Flows Using Machine Learning Algorithms

• Autor(a) principal: Kakihata, Eduardo Massato
• Data de Publicação: 2017
• Outros Autores: Sapia, Helton Molina, Oiakawa, Ronaldo Toshiaki, Pereira, Danillo Roberto, Papa, Joao Paulo [UNESP], De Albuquerque, Victor Hugo Costa, Da Silva, Francisco Assis
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/TLA.2017.8071245; http://hdl.handle.net/11449/179313
• topic: Bayes Classifier; Intrusion Detection System; KNN; Machine Learning; Netflow; OPF; SVM

The use of technology information and communication by different types of devices generates a large quantity of data packets that contains of confidential and personal information. The traffic of data packet can be summarized in network flow. Due this reason, it is necessary to use computer security tools, such as Intrusion Detection Systems (IDS). This work presents an IDS that can perform the flow- based analysis (netflow). This research conducted an analysis on flows previously collected and properly detected of three different types of attacks. The flows were organized to be processed by machine learning methods. The results obtained by proposed approach were very promising. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster IDS-related research.