| Título: | Software Vulnerability Analysis Method Based on Adaptive-K Sequence Clustering |
| Autores: |
Wu, Di; Yanshan University Ren, Jiadong; Yanshan University |
| Fecha: | 2013-12-29 |
| Publicador: | TELKOMNIKA: Indonesian journal of electrical engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: |
Computer Engineering; Knowledge discovery in data Software vulnerability analysis; Sequence clustering; Adaptive-K; False positive rate |
| Descripción: | Software vulnerability analysis has become a hot topic recently. However, the traditional methods for analyzing software vulnerability have higher false positive rate. In this paper, adaptive K function is defined, and SVAAKSC (Software vulnerability analysis method based on adaptive-K sequence clustering) is presented. The collected objects in software vulnerability sequence database SVSD are pretreated to equal length vectors. Moreover, according to adaptive-K based sequence clustering algorithm, all software vulnerabilities in SVSD are clustered into K clustering. Afterwards, by matching the similarities between detected vulnerability from software and each clustering center, whether the detected vulnerability is a real software vulnerability can be judged. Finally, the corresponding analysis report is obtained. The experimental results and analysis show that SVAAKSC has lower false positive rate and better analysis time. |
| Idioma: | No aplica |