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Proceedings Paper

Regularization in the synthesis of host-based anomaly detectors
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Paper Abstract

The goal of host-based intrusion detection is to detect attacks against a single information system. Many host-based intrusion detector systems - especially those that use anomaly detection - use training data to synthesize detectors automatically, that is, the detectors are classifiers created by machine learning. Regularization, which often improves the performance of machine learning algorithms, has not previously been applied to intrusion detector synthesis. This paper discusses regularization for machine learning-based intrusion detectors, showing how regularization can be accomplished for such systems and providing the results of an empirical evaluation.

Paper Details

Date Published: 8 August 2003
PDF: 12 pages
Proc. SPIE 5107, System Diagnosis and Prognosis: Security and Condition Monitoring Issues III, (8 August 2003); doi: 10.1117/12.488365
Show Author Affiliations
Christoph C. Michael, Cigital Labs. (United States)


Published in SPIE Proceedings Vol. 5107:
System Diagnosis and Prognosis: Security and Condition Monitoring Issues III
Peter K. Willett; Thiagalingam Kirubarajan, Editor(s)

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