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Any vulnerability in the software creates a software security threat and helps hackers to gain unauthorized access to resources. Vulnerability prediction models help software engineers to effectively allocate their resources to find any vulnerable class in the software before its delivery to customers. Vulnerable classes must be carefully reviewed by security experts and tested to identify potential threats that may arise in the future. In the present work, a novel technique based on grey wolf algorithm and random forest is proposed for software vulnerability prediction. Grey wolf technique is a metaheuristic technique, and it is used to select the best subset of features. The proposed technique is compared with other machine learning techniques. Experiments were performed on three datasets available publicly. It was observed that the proposed technique (GW-RF) outperformed all other techniques for software vulnerability prediction.

Software Vulnerability Prediction Using Grey Wolf-Optimized Random Forest on the Unbalanced Data Sets