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.
Abstract (translated)
软件漏洞是网络攻击普遍存在的根本原因，而识别软件漏洞是网络安全中一个关键但具有挑战性的问题。在本文中，我们针对Python编程语言，应用并比较了不同的机器学习算法来进行源代码漏洞检测。我们的实验评估结果表明，我们的双向长短时记忆（BiLSTM）模型取得了显著的性能（平均准确率=98.6%，平均F1分数=94.7%，平均精确率=96.2%，平均召回率=93.3%，平均准确率=99.3%)，从而为Python源代码漏洞检测树立了一个新的基准。
URL
https://arxiv.org/abs/2404.09537
