论文标题
语义保留的强化学习攻击针对图形神经网络进行恶意软件检测
Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection
论文作者
论文摘要
由于已经提出了越来越多的基于研究的恶意软件扫描仪，因此发现现有的逃避技术，包括代码混淆和多态恶意软件，被发现效率较低。在这项工作中，我们提出了一种基于增强学习的语义性传播（即提供功能性的）攻击，以实现恶意软件检测的黑盒GNN（GraphNeural网络）。通过语义NOPS插入来生成对抗性恶意软件的关键因素是选择适当的Semanticnops及其相应的基本块。拟议的攻击使用强化学习自动做出这些“如何选择”决策。为了评估攻击，我们已经培训了两种类型的GNN，这些GNN具有五种类型的Windows恶意软件样本和各种良性Windows程序的五种类型（即后门，Trojan-Downloader，Trojan-ransom，Adware和Worm）。评估结果表明，所提出的攻击可以比三个基线攻击实现明显更高的逃避率，即传承语义的随机说明插入攻击，传承语义的累积指导插入攻击以及基于语义的基于基于基于梯度的插入插入插入攻击。
As an increasing number of deep-learning-based malware scanners have been proposed, the existing evasion techniques, including code obfuscation and polymorphic malware, are found to be less effective. In this work, we propose a reinforcement learning-based semantics-preserving (i.e.functionality-preserving) attack against black-box GNNs (GraphNeural Networks) for malware detection. The key factor of adversarial malware generation via semantic Nops insertion is to select the appropriate semanticNopsand their corresponding basic blocks. The proposed attack uses reinforcement learning to automatically make these "how to select" decisions. To evaluate the attack, we have trained two kinds of GNNs with five types(i.e., Backdoor, Trojan-Downloader, Trojan-Ransom, Adware, and Worm) of Windows malware samples and various benign Windows programs. The evaluation results have shown that the proposed attack can achieve a significantly higher evasion rate than three baseline attacks, namely the semantics-preserving random instruction insertion attack, the semantics-preserving accumulative instruction insertion attack, and the semantics-preserving gradient-based instruction insertion attack.
