A Categorization Framework for Common Computer Vulnerabilities and Exposures
The dictionary of common vulnerabilities and exposures (CVEs) is a compilation of known security loopholes whose objective is to both facilitate the exchange of security-related information and expedite vulnerability analysis of computer systems. Its lack of categorization and generalization capability renders the dictionary ineffective when it comes to developing defense strategies for clustered vulnerabilities instead of individual exploits. To address this issue, we propose a CVE categorization framework termed CVE Classifier that transforms the dictionary into a classifier that not only categorizes CVEs with respect to diverse taxonomic features but can also evaluate general trends in the evolution of vulnerabilities. With the help of support vector machines, CVE Classifier builds learning models for taxonomic features based on training data automatically extracted from pertinent vulnerability databases including BID, X-Force and Secunia, and CVE entries containing telltale keywords unique to taxonomic features. We use word-stemming and stopword-removal techniques to reduce the dimensions of the feature space formed by CVEs and develop a data fusion and cleansing process to eliminate data inconsistencies to improve classification performance. The CVE classification produced by the proposed framework reveals that the majority of the Internet security loopholes are harbored by a small set of services. Moreover, it becomes evident that the widespread deployment of security devices provides many additional attack points as such devices demonstrate a great mount of vulnerabilities. Finally, the CVE Classifier points out that remotely exploitable security loopholes continue to dominate the CVEs landscape.
common vulnerabilities and exposures (cves) taxonomic features and categorization support vector machine (svm) classification accuracy and f-measure