Categorization of CVE Based on Vulnerability Software By Using Machine Learning Techniques
Aneela Kiran, Samina Rajper, Riaz Ahmed Shaikh, Ali Shah, Shahid Hussain Danwar, M Yldrm, E Geer, &akgl, T Lancet, Y Qiu, Y Liu, A Liu, J Zhu, J &xu, Al-Msie'deen, ' Ra, Fat, A Dobrovoljc, D Trek, B Likar, Seng, Lim &ithnin, Norafida&shaid, Syed, Taneeya Satyapanich, Tim Finin, Francis Ferraro, N Polatidis, E Pimenidis, M Pavlidis, S Papastergiou, H &mouratidis, M Almukaynizi, E Marin, E Nunes, P Shakarian, G Simari, D Kapoor, T &siedlecki, J Ruohonen, Q Chen, L Bao, L Li, X Xia, L Cai
Abstract
Public Platform is designed as an online website for researchers to collect reliable data for the study. NVD plays a significant role in analyzing The result of analysis in association influence metrics CVSS, type of CWE and applicability reports weakness CPE. The vulnerability testing is not performed by NVD while third-party security researchers and vulnerability controllers give information that has been assigned these attributes. ML plays a significant part in our daily life for the classification of huge data and is giving fruitful results. Because of that result, major steps have been made against criminal activities or unauthorized use of electronic data and protect the data from attackers. The major goal of this research is to categorize CVE Based Vulnerability Software throughout the last two years, 2019-2020.The findings of this study were used to ML for the categorization of CVE and compared and will open door for the fresh researchers and professionals.