Output details
11 - Computer Science and Informatics
Brunel University London
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Output 0 of 0 in the submission
Article title
Effects-Based Feature Identification for Network Intrusion Detection
Type
D - Journal article
Title of journal
Neurocomputing
Article number
-
Volume number
121
Issue number
-
First page of article
265
ISSN of journal
09252312
Year of publication
2013
Number of additional authors
2
Additional information
<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).
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
