期刊
RECENT DEVELOPMENTS IN MACHINE LEARNING AND DATA ANALYTICS
卷 740, 期 -, 页码 249-260出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-981-13-1280-9_24
关键词
Alert correlation; Granger causality test; Intrusion detection system; Attack scenario; Feature ranking; Feature selection
This paper presents an effective alert correlation method referred to as MaNaDAC to support network intrusion detection. The method includes several modules such as feature ranking and selection, clustering and fusion to process low-level alerts and uses the concept of causality to discover relations among attacks. The method has been validated using DARPA 2000 intrusion dataset.
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