Output details
11 - Computer Science and Informatics
Brunel University London
Effects-Based Feature Identification for Network Intrusion Detection
<15> The work allows cyber attacks to be pinpointed with a high degree of accuracy within the cluttered and conflicted network environment. Unlike previous research in this area, this approach shows that a statistically relevant and reduced feature set filters out the noisy data associated with non-relevant features thus enabling the identification of the specific features that characterise the cyber attack. This research was funded by MoD/Dstl as part of their Cyber Network Defence Watchtower project to enhance Computer Network Defence (CND) capability based upon Best of Breed CND tools for the UK GOSCC (Global Operations Security Control Centre).