A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data (2002)
| Venue: | Applications of Data Mining in Computer Security |
| Citations: | 130 - 8 self |
BibTeX
@INPROCEEDINGS{Eskin02ageometric,
author = {Eleazar Eskin and Andrew Arnold and Michael Prerau and Leonid Portnoy and Sal Stolfo},
title = {A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data},
booktitle = {Applications of Data Mining in Computer Security},
year = {2002},
publisher = {Kluwer}
}
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Abstract
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. In our framework, data elements are mapped to a feature space which is typically a vector space! d. Anomalies are detected by determining which points lies in sparse
