| Full text | |
| Author(s): |
Pereira, Clayton
;
Nakamura, Rodrigo
;
Papa, Joao Paulo
;
Costa, Kelton
;
Pfeifer, T
;
Jayasumana, A
;
Aschenbruck, N
Total Authors: 7
|
| Document type: | Journal article |
| Source: | 2011 IEEE 36TH CONFERENCE ON LOCAL COMPUTER NETWORKS (LCN); v. N/A, p. 4-pg., 2011-01-01. |
| Abstract | |
Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. Neural networks and Support Vector Machines have been also extensively applied to this task. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In this research, we introduce a new pattern classifier named Optimum-Path Forest (OPF) to this task, which has demonstrated to be similar to the state-of-the-art pattern recognition techniques, but extremely more efficient for training patterns. Experiments on public datasets showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, as well as allow the algorithm to learn new attacks faster than the other techniques. (AU) | |
| FAPESP's process: | 09/16206-1 - New trends on optimum-path forest-based pattern recognition |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 10/02045-3 - Intrusion Detection Based on Optimum-Path Forest |
| Grantee: | Clayton Reginaldo Pereira |
| Support Opportunities: | Scholarships in Brazil - Master |