An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
Machine Learning
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
RULE BASED ANALYSIS OF COMPUTER SECURITY
RULE BASED ANALYSIS OF COMPUTER SECURITY
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
MulVAL: a logic-based network security analyzer
SSYM'05 Proceedings of the 14th conference on USENIX Security Symposium - Volume 14
Generalized inverse document frequency
Proceedings of the 17th ACM conference on Information and knowledge management
Vulnerability analysis for a quantitative security evaluation
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Vulnerability Management
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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The research on network vulnerability analysis and management has gained increased attention during last decade since many studies have proved that combination of exploits is typical means to compromise a network system. This paper presents an intelligent method for analyzing and classifying vulnerabilities based on text mining technology. The proposed mechanism can automatically classify vulnerabilities into different predefined categories and obtain valuable information from abundant vulnerability texts. A series of experiments on 1060 new reported vulnerabilities in last three years by CERT are performed to demonstrate the efficiency of this mechanism. The results generated by this study can be applied to detecting multistage attack, correlating intrusion alerts, and generating attack graph.