Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights

• URL: http://arxiv.org/abs/2108.12473v1
• Date: Fri, 27 Aug 2021 19:42:13 GMT
• Title: Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights
• Authors: Omid Kargarnovin, Amir Mahdi Sadeghzadeh, and Rasool Jalili
• Abstract summary: We present a black-box source code-based adversarial malware generation approach that can be used to evaluate the robustness of malware detection models against real-world adversaries. We then propose Mal2GCN, a robust malware detection model. Mal2GCN uses the representation power of graph convolutional networks combined with the non-negative weights training method to create a malware detection model with high detection accuracy.
• Score: 1.3190581566723918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing pace of using machine learning to solve various problems, securing these models against adversaries has become one of the main concerns of researchers. Recent studies have shown that in an adversarial environment, machine learning models are vulnerable to adversarial examples, and adversaries can create carefully crafted inputs to fool the models. With the advent of deep neural networks, many researchers have used deep neural networks for various tasks, and have achieved impressive results. These models must become robust against attacks before being deployed safely, especially in security-related fields such as malware detection. In this paper, we first present a black-box source code-based adversarial malware generation approach that can be used to evaluate the robustness of malware detection models against real-world adversaries. The proposed approach injects adversarial codes into the various locations of malware source codes to evade malware detection models. We then propose Mal2GCN, a robust malware detection model. Mal2GCN uses the representation power of graph convolutional networks combined with the non-negative weights training method to create a malware detection model with high detection accuracy, which is also robust against adversarial attacks that add benign features to the input.

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