BMOP: Bidirectional Universal Adversarial Learning for Binary OpCode Features

For malware detection, current state-of-the-art research concentrates on machine learning techniques. Binary n-gram OpCode features are commonly used for malicious code identification and classification with high accuracy. Binary OpCode modification is much more difficult than modification of image pixels. Traditional adversarial perturbation methods could not be applied on OpCode directly. In this paper, we propose a bidirectional universal adversarial learning method for effective binary OpCode perturbation from both benign and malicious perspectives. Benign features are those OpCodes that represent benign behaviours, while malicious features are OpCodes for malicious behaviours. From a large dataset of benign and malicious binary applications, we select the most significant benign and malicious OpCode features based on the feature SHAP value in the trained machine learning model. We implement an OpCode modification method that insert benign OpCodes into executables as garbage codes without execution and modify malicious OpCodes by equivalent replacement preserving execution semantics. The experimental results show that the benign and malicious OpCode perturbation (BMOP) method could bypass malicious code detection models based on the SVM, XGBoost, and DNN algorithms