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About anomaly detection in computer networks using optimum-path forest: advances and applications in computer networks

Abstract

In the last years, the number of people interested in to access unauthorized information is growing exponentially together with the different number of attacks that come at the same speed. Although we have tools like as antivirus and firewall that may deal with this problem, the large number of new anomalies and attacks may turn such devices non-effective. Therefore, considering the amount of diversity of these attacks, some companies have increased their investments in studies for developing more effective intrusion detection systems using artificial intelligence techniques. This proposal aims at studying and developing anomaly detection techniques in computer networks by means of the Optimum-Path Forest (OPF) classifier, which has not been used to this context so far. In addition, the project aims at developing a library for the OPF classifier in order to broaden its dissemination among nationals and international researchers. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
PASSOS JUNIOR, LEANDRO APARECIDO; OBA RAMOS, CAIO CESAR; RODRIGUES, DOUGLAS; PEREIRA, DANILLO ROBERTO; DE SOUZA, ANDRE NUNES; PONTARA DA COSTA, KELTON AUGUSTO; PAPA, JOAO PAULO. Unsupervised non-technical losses identification through optimum-path forest. Electric Power Systems Research, v. 140, p. 413-423, NOV 2016. Web of Science Citations: 10.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.