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The deployment of deep learning-based malware detection systems has transformed cybersecurity, offering sophisticated pattern recognition capabilities that surpass traditional signature-based approaches. However, these systems introduce new vulnerabilities requiring systematic investigation. This chapter examines adversarial attacks against graph neural network-based malware detection systems, focusing on semantics-preserving methodologies that evade detection while maintaining program functionality. We introduce a reinforcement learning (RL) framework that formulates the attack as a sequential decision making problem, optimizing the insertion of no-operation (NOP) instructions to manipulate graph structure without altering program behavior. Comparative analysis includes three baseline methods: random insertion, hill-climbing, and gradient-approximation attacks. Our experimental evaluation on real world malware datasets reveals significant differences in effectiveness, with the reinforcement learning approach achieving perfect evasion rates against both Graph Convolutional Network and Deep Graph Convolutional Neural Network architectures while requiring minimal program modifications. Our findings reveal three critical research gaps: transitioning from abstract Control Flow Graph representations to executable binary manipulation, developing universal vulnerability discovery across different architectures, and systematically translating adversarial insights into defensive enhancements. This work contributes to understanding adversarial vulnerabilities in graph-based security systems while establishing frameworks for evaluating machine learning-based malware detection robustness.
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COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection
Despite many attempts, the state-of-the-art of adversarial machine learning on malware detection systems generally yield unexecutable samples. In this work, we set out to examine the robustness of visualization-based malware detection system against adversarial examples (AEs) that not only are able to fool the model, but also maintain the executability of the original input. As such, we first investigate the application of existing off-the-shelf adversarial attack approaches on malware detection systems through which we found that those approaches do not necessarily maintain the functionality of the original inputs. Therefore, we proposed an approach to generate adversarial examples, COPYCAT, which is specifically designed for malware detection systems considering two main goals; achieving a high misclassification rate and maintaining the executability and functionality of the original input. We designed two main configurations for COPYCAT, namely AE padding and sample injection. While the first configuration results in untargeted misclassification attacks, the sample injection configuration is able to force the model to generate a targeted output, which is highly desirable in the malware attribution setting. We evaluate the performance of COPYCAT through an extensive set of experiments on two malware datasets, and report that we were able to generate adversarial samples that are misclassified at a rate of 98.9% and 96.5% with Windows and IoT binary datasets, respectively, outperforming the misclassification rates in the literature. Most importantly, we report that those AEs were executable unlike AEs generated by off-the-shelf approaches. Our transferability study demonstrates that the generated AEs through our proposed method can be generalized to other models.
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- Award ID(s):
- 1841520
- PAR ID:
- 10398661
- Date Published:
- Journal Name:
- arXivorg
- ISSN:
- 2331-8422
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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