A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data

E Eskin; A Arnold; M Prerau; L Portnoy; S Stolfo

Conference Proceedings

A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data

Eskin E
Arnold A
Prerau M
et al.

Applications of Data Mining in Computer Security (2002) 6 77–102

N/ACitations

146Readers

Abstract

Most current intrusion detection systems employ signature-based methods or data mining-based methods which rely on labeled training data. This training data is typically expensive to produce. We present a new geometric framework for unsupervised anomaly detection, which are algorithms that are designed to process unlabeled data.

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APA

Eskin, E., Arnold, A., Prerau, M., Portnoy, L., & Stolfo, S. (2002). A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. In Applications of Data Mining in Computer Security (Vol. 6, pp. 77–102). Kluwer. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:A+geometric+framework+for+unsupervised+anomaly+detection:+Detecting+intrusions+in+unlabeled+data#0

A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data

Abstract

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