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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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Format:PDFSize:1,331 KB
Date:Jan 2006
Pages:10

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