Softare Vulnerability Prediction using Extreme Learning Machines Algorithm

Abstract

Software vulnerability prediction aims to detect vulnerabilities in the source code before the software is deployed into the operational environment. The accurate prediction of vulnerabilities helps to allocate more testing resources to the vulnerability-prone modules. From the machine learning perspective, this problem is a binary classification task which classifies software modules into vulnerability-prone and non-vulnerability-prone categories. Several machine learning models have been built for addressing the software vulnerability prediction problem, but the performance of the state-of-the-art models is not yet at an acceptable level. In this study, we aim to improve the performance of software vulnerability prediction models by using Extreme Learning Machines (ELM) algorithms which have not been investigated for this problem. Before we apply ELM algorithms for selected three public datasets, we use data balancing algorithms to balance the data points which belong to two classes. We discuss our initial experimental results and provide the lessons learned. In particular, we observed that ELM algorithms have a high potential to be used for addressing the software vulnerability prediction problem.