论文标题
用于检测PHP漏洞的混合图神经网络方法
A Hybrid Graph Neural Network Approach for Detecting PHP Vulnerabilities
论文作者
论文摘要
本文提出了深度攻击性，一种深度学习方法，可检测PHP源代码中的漏洞。我们的方法实现了一种新型的混合技术，该技术结合了封闭式复发单元和图形卷积网络，以检测SQLI，XSS和OSCI脆弱性利用语法和语义信息。我们在建立的合成数据集和从GitHub收集的新型现实世界数据集上评估并将其与艺术的状态进行了比较。实验结果表明，在合成数据集上，深度效率接近完美的分类，而现实数据集的F1得分为88.12％，表现优于相关方法。我们通过在既定的WordPress插件中发现4个新颖的漏洞来证实野外的深度攻击。
This paper presents DeepTective, a deep learning approach to detect vulnerabilities in PHP source code. Our approach implements a novel hybrid technique that combines Gated Recurrent Units and Graph Convolutional Networks to detect SQLi, XSS and OSCI vulnerabilities leveraging both syntactic and semantic information. We evaluate DeepTective and compare it to the state of the art on an established synthetic dataset and on a novel real-world dataset collected from GitHub. Experimental results show that DeepTective achieves near perfect classification on the synthetic dataset, and an F1 score of 88.12% on the realistic dataset, outperforming related approaches. We validate DeepTective in the wild by discovering 4 novel vulnerabilities in established WordPress plugins.
