Vulnerability Detection in Open Source Software Using Machine Learning

Abstract

Open Source Software (OSS) has become the pillar of modern computing, acting as the backbone to a wide variety of applications serving web platforms, embedded systems, and more. Although OSS offers innovation, cost-effectiveness, and prompt deployment, it is also vulnerable to security attacks due to its open access and community creation. A long-established approach to determining the weakness of OSS has been based on manual reviews of the code, static analysis and bug reports provided by the community. Yet, these methods can be very time-consuming and error-prone, and are not suited to the size of current OSS projects. Recently, progress in Machine Learning (ML) has opened up new possibilities for the automated and intelligent detection of vulnerabilities. With the analysis of code semantics, dependencies, and patterns in large amounts of data, it is also possible to reliably predict and classify which components are vulnerable using ML models. The current paper discusses how ML methods can be applied with the purpose of identifying vulnerabilities in OSS. These can be listed as: (i) learning the limitations of the current manual and semi-automated vulnerability detection methods, (ii) investigating the ML method of analysing source code and making use of supervised learning, deep learning, and Natural Language Processing (NLP), and (iii) determining how effective ML can be employed in OSS projects in the real world. The procedure will boil down to gathering the dataset from off-the-shelf sources (public repositories, e.g., GitHub, CVE databases), preprocessing (embedding the code into a vector), training the ML classifiers, and evaluating the performance with different levels of precision, recall, and the F1-score. Interpretability-related problems are also explored by us so that the ML-based detection could be transparent and trustworthy. Our experiments show that ML models outperform static analysis tools, achieving higher accuracy, and are faster than traditional static analysis tools. Deep learning models make use of Abstract Syntax Trees (AST) and code2vec embedding that are more effective at detecting zero-day vulnerabilities than rules-based ones. The presentation discusses the problems of imbalanced datasets, adversarial attacks on machine learning models, and explainability in security-relevant areas. The conclusion points to the demand for hybrid solutions that use ML in combination with existing static data-driven analysis to identify vulnerable OSS. Lastly, we outline future research directions, including federated learning for remote OSS communities, explainable AI (XAI) to enhance transparency, and reinforcement learning to improve adaptability in detecting vulnerabilities