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KDD
2002
ACM

ADMIT: anomaly-based data mining for intrusions

11 years 4 months ago
ADMIT: anomaly-based data mining for intrusions
Security of computer systems is essential to their acceptance and utility. Computer security analysts use intrusion detection systems to assist them in maintaining computer system security. This paper deals with the problem of differentiating between masqueraders and the true user of a computer terminal. Prior efficient solutions are less suited to real time application, often requiring all training data to be labeled, and do not inherently provide an intuitive idea of what the data model means. Our system, called ADMIT, relaxes these constraints, by creating user profiles using semiincremental techniques. It is a real-time intrusion detection system with host-based data collection and processing. Our method also suggests ideas for dealing with concept drift and affords a detection rate as high as 80.3% and a false positive rate as low as 15.3%.
Karlton Sequeira, Mohammed Javeed Zaki
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2002
Where KDD
Authors Karlton Sequeira, Mohammed Javeed Zaki
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