Machine Learning Techniques for Python Source Code Vulnerability Detection
This addresses the crucial cybersecurity challenge of identifying software vulnerabilities for Python developers and security analysts, though it appears incremental as it applies existing methods to a specific language.
The paper tackles the problem of detecting vulnerabilities in Python source code by applying and comparing different machine learning algorithms, with their BiLSTM model achieving high performance metrics including 98.6% accuracy and 99.3% ROC.
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.