Output details
11 - Computer Science and Informatics
Birmingham City University
Article title
Filtering intrusion detection alarms
Type
D - Journal article
Title of journal
Cluster Computing
Article number
-
Volume number
13
Issue number
1
First page of article
19
ISSN of journal
1573-7543
Year of publication
2009
Number of additional authors
2
Additional information
<19> Due to its potential impact for enabling better cybersecurity protection for government, industry, and commercial organizations, the goal of detecting abnormal behaviour is currently of great interest to the security research community. Research in this article makes this point clearly by designing an innovative decision support tool, based on a self-organizing neural network, to make the process of intrusion detection more accurate. The empirical results demonstrate the advantage of using this approach.
Interdisciplinary
-
Cross-referral requested
-
Research group
1 - Cyber Security
Citation count
3
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
