Association Rule Mining and Evaluation Based on Information Security Vulnerabilities Main Body

Abstract:

Information security is a matter of concern in any sector and industry, and the vulnerability is the important factor which caused this issue. Therefore it is necessary to analyze and predict the occurrence of vulnerability. This paper used the datas of CNNVD vulnerability database and Apriori algorithm to analyze and predict the occurrence of software vulnerability. In the data preprocessing stage by changing the level of vulnerability rule we can dig out more concept association. In the evaluation stage of association rules by designing filters we can find the rules in line with the degree of user interest. Finally, this papper could demonstrate the effectiveness of of this method by experiments.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 1282-1285
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.1282
• Online since: November 2014
• Authors: Ying Sui*
• Keywords: Apriori Association Algorithm, Data Mining (DM), Information Security Vulnerabilities, Vulnerability Body

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