DocumentCode :
1324099
Title :
Fuzzy Classification Metrics for Scanner Assessment and Vulnerability Reporting
Author :
Loh, Peter Kok Keong ; Subramanian, Deepak
Author_Institution :
Comput. Security Lab., Nanyang Technol. Univ., Singapore, Singapore
Volume :
5
Issue :
4
fYear :
2010
Firstpage :
613
Lastpage :
624
Abstract :
In information security, web application scanners detect and provide some diagnoses for specific vulnerabilities. However, scanner performance as well as the damage potential of different vulnerabilities varies. This undermines the development of effective remediation solutions and the reliable sharing of vulnerability information. This paper describes an approach based on soft computing technology for the development of metrics that are used to grade web application scanners and vulnerabilities so that scanner performance can be evaluated and confidence levels can be computed for vulnerability reports. These metrics help derive a level of assurance that will support security management decisions, enhance effective remediation efforts, and could serve as security tool design metrics.
Keywords :
Internet; fuzzy set theory; neural nets; pattern classification; security of data; software metrics; software reliability; Web application scanners; fuzzy classification metrics; information security; scanner assessment; security management decisions; security tool design metrics; soft computing technology; vulnerability reporting; Classification algorithms; Fuzzy logic; Intrusion detection; Measurement; Software; Confidence level; Fuzzy classifiers; scanner; vulnerability; web application;
fLanguage :
English
Journal_Title :
Information Forensics and Security, IEEE Transactions on
Publisher :
ieee
ISSN :
1556-6013
Type :
jour
DOI :
10.1109/TIFS.2010.2075926
Filename :
5570991
Link To Document :
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