Leveraging one-class SVM and semantic analysis to detect anomalous content (2005)
| Venue: | In Proceedings of IEEE International Conference on Intelligence and Security Informatics (ISI 2005 |
| Citations: | 3 - 0 self |
BibTeX
@INPROCEEDINGS{Yilmazel05leveragingone-class,
author = {Ozgur Yilmazel and Svetlana Symonenko and Niranjan Balasubramanian and Elizabeth D. Liddy},
title = {Leveraging one-class SVM and semantic analysis to detect anomalous content},
booktitle = {In Proceedings of IEEE International Conference on Intelligence and Security Informatics (ISI 2005},
year = {2005},
pages = {381--388},
publisher = {Springer}
}
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Abstract
Experiments were conducted to test several hypotheses on methods for improving document classification for the malicious insider threat problem within the Intelligence Community. Bag-of-words (BOW) representations of documents were compared to Natural Language Processing (NLP) based representations in both the typical and one-class classification problems using the Support Vector Machine algorithm. Results show that the NLP features significantly improved classifier performance over the BOW approach both in terms of precision and recall, while using many fewer features. The one-class algorithm using NLP features demonstrated robustness when tested on new domains. 1
