Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
406722 | Neurocomputing | 2013 | 9 Pages |
Abstract
Intrusion detection systems (IDS) are an important element in a network's defences to help protect against increasingly sophisticated cyber attacks. IDS that rely solely on a database of stored known attacks are no longer sufficient for effectively detecting modern day threats. This paper presents a novel anomaly detection technique that can be used to detect previously unknown attacks on a network by identifying attack features. This effects-based feature identification method uniquely combines k-means clustering, Naïve Bayes feature selection and C4.5 decision tree classification for pinpointing cyber attacks with a high degree of accuracy in order to increase the situational awareness of cyber network operators.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Panos Louvieris, Natalie Clewley, Xiaohui Liu,