| Grant number: | 15/00801-9 |
| Support Opportunities: | Regular Research Grants |
| Start date: | July 01, 2015 |
| End date: | June 30, 2017 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Kelton Augusto Pontara da Costa |
| Grantee: | Kelton Augusto Pontara da Costa |
| Host Institution: | Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil |
| City of the host institution: | Bauru |
| Associated researchers: | João Paulo Papa |
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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