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Adaptive Intrusion Detection Based on Machine Learning: Feature Extraction, Classifier Construction and Sequential Pattern Prediction

Overview In recent years, intrusion detection has emerged as an important technique for network security. Due to the large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, to optimize the performance of Intrusion Detection Systems (IDSs) becomes an important open problem. In this paper, a general framework of adaptive intrusion detection based on machine learning is presented. In the framework, three perspectives of challenging problems are explored, which include feature extraction, classifier construction and pattern prediction for sequential data.

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Publisher
National University of Defense Technology
File Format
PDF
Date Published
Oct 14, 2008
Format
White Papers
Topics
Intrusion Detection Systems, Artificial Intelligence, Network Security

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