Machine Learning Techniques for Python Source Code Vulnerability Detection

Talaya Farasat; Joachim Posegga

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arXiv · 2404.09537

Machine Learning Techniques for Python Source Code Vulnerability Detection

Talaya Farasat·Joachim Posegga

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Abstract

Software vulnerabilities are a fundamental reason for the prevalence of cyber attacks and their identification is a crucial yet challenging problem in cyber security. In this paper, we apply and compare different machine learning algorithms for source code vulnerability detection specifically for Python programming language. Our experimental evaluation demonstrates that our Bidirectional Long Short-Term Memory (BiLSTM) model achieves a remarkable performance (average Accuracy = 98.6%, average F-Score = 94.7%, average Precision = 96.2%, average Recall = 93.3%, average ROC = 99.3%), thereby, establishing a new benchmark for vulnerability detection in Python source code.

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Talaya Farasat, Joachim Posegga. 2024-04-15. Machine Learning Techniques for Python Source Code Vulnerability Detection. https://arxiv.org/abs/2404.09537

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