Software vulnerability prioritization using vulnerability description

Whenever a vulnerability is detected by the testing team, it is described based on its characteristics and a detailed overview of the vulnerability is given by the testing team. Usually, there are certain features or keywords that points towards the possible severity level of a vulnerability. Using these keywords in the vulnerability description, a possible estimation of the severity level of vulnerabilities can be given just by their description. In this paper, we are eliminating the need for generating a severity score for software vulnerabilities by using the description of a vulnerability for their prioritization. This study makes use of word embedding and convolution neural network (CNN). The CNN is trained with sufficient samples vulnerability descriptions from all the categories, so that it can capture discriminative words and features for the categorization task. The proposed system helps to channelize the efforts of the testing team by prioritizing the newly found vulnerabilities in three categories based on previous data. The dataset includes three data samples from three different vendors and two mixed vendor data samples.