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8 August 2003 Regularization in the synthesis of host-based anomaly detectors
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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.
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Christoph C. Michael "Regularization in the synthesis of host-based anomaly detectors", Proc. SPIE 5107, System Diagnosis and Prognosis: Security and Condition Monitoring Issues III, (8 August 2003); https://doi.org/10.1117/12.488365
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