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| Technical papers published in the field of Information Security. |
Model Selection and Validation for Intrusion Detection Systems
This paper describes results concerning the robustness and generalization capabilities of a supervised machine learning methods in detecting
intrusions using network audit trails. This paper presents a comparative study of using neural networks, support vector machines (SVM's), multivariate
adaptive regression splines (MARS), classification and regression trees (CART), TreeNet and linear genetic programs (LGP's) for intrusion detection. We investigate and
compare the performance of IDS's based on the mentioned techniques, with respect to a well-known set of intrusion evaluation data.
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Detection of Decoys and Virtual Environments (Honeypots and Honeynets)
This paper focuses on the detection of virtual environments and low interaction honeypots by using a feature set that is built using traditional
system and network level finger printing mechanisms. Earlier work in the area has been mostly based on the system level detection.
The results aim at bringing out the limitations in the current honeypot technology. This paper also describes the results concerning the
robustness and generalization capabilities of kernel methods in detecting honeypots using system and network finger printing data.
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