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- W2107157501 abstract "In the field of network intrusion detection, both the signature-based intrusion detec-tion system and the machine learning-based intrusion detection system possess advan-tages and disadvantages. When the two discrepant systems are combined in a way that the former is used as the main system and the latter as a supporting system, the machine learning-based intrusion detection system measures the validity of alarms determined by the signature-based intrusion detection system and filters out any false alarms. What is more, such an approach can also detect attacks that the signature-based system by itself cannot detect. The objective of this paper is to propose a combined model of the signature-based and machine learning-based intrusion detection systems and to show that the combined system is more efficient than each individual system. We used the DARPA Data Set in experiments in order to show the usefulness of the combined model. Snort was used for the experiment as a signature-based intrusion detection system and extended IBL (In-stance-based Learner) was used as the principal learning algorithm for the machine learning-based intrusion detection system. To compare performances of the algorithms, C4.5 was used." @default.
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- W2107157501 date "2006-01-01" @default.
- W2107157501 modified "2023-09-23" @default.
- W2107157501 title "Effective intrusion detection model through the combination of a signature-based intrusion detection system and a machine learning-based intrusion detection system" @default.
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