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A Novel Cluster-based Intrusion Detection Approach Integrating Multiple Learning Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 166 - Number 3 |
| Year of Publication: 2017 |
| Authors: Hossein Shapoorifard, Pirooz Shamsinejad |
10.5120/ijca2017913948
|
Hossein Shapoorifard, Pirooz Shamsinejad . A Novel Cluster-based Intrusion Detection Approach Integrating Multiple Learning Techniques. International Journal of Computer Applications. 166, 3 ( May 2017), 13-16. DOI=10.5120/ijca2017913948
Abstract
In order to make computer systems completely secure, in addition to firewalls and other intrusion protection devices, other systems called intrusion detection systems (IDS) are needed to detect intrusion and provide solutions to counter the intruder if he penetrated through firewall, antivirus and other security devices. Many IDS have been developed based on machine learning techniques. Specifically, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning techniques. This paper proposes an improvement for a feature representation approach, namely the cluster center and nearest neighbor (CANN) approach.
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