Risk Factors to Retrieve Anomaly Intrusion Information and Profile User Behavior

Risk Factors to Retrieve Anomaly Intrusion Information and Profile User Behavior

Yun Wang (Yale University & Yale-New Haven Health System and Qualidigm, USA) and Lee Seidman (Qualidigm, USA)
DOI: 10.4018/jbdcn.2006010104
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Abstract

The use of network traffic audit data for retrieving anomaly intrusion information and profiling user behavior has been studied previously, but the risk factors associated with attacks remain unclear. This study aimed to identify a set of robust risk factors via the bootstrap resampling and logistic regression modeling methods based on the KDD-cup 1999 data. Of the 46 examined variables, 16 were identified as robust risk factors, and the classification showed similar performances in sensitivity, specificity, and correctly classified rate in comparison with the KDD-cup 1999 winning results that were based on a rule-based decision tree algorithm with all variables. The study emphasizes that the bootstrap simulation and logistic regression modeling techniques offer a novel approach to understanding and identifying risk factors for better information protection on network security.

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