MaliFuzz: Adversarial Malware Detection Model for Defending Against Fuzzing Attack

With the prevalence of machine learning in malware defense, hackers have tried to attack machine learning models to evade detection. It is generally difficult to explore the details of malware detection models, hackers can adopt fuzzing attack to manipulate the features of the malware closer to benign programs on the premise of retaining their functions. In this paper, attack and defense methods on malware detection models based on machine learning algorithms were studied. Firstly, we designed a fuzzing attack method by randomly modifying features to evade detection. The fuzzing attack can effectively descend the accuracy of machine learning model with single feature. Then an adversarial malware detection model MaliFuzz is proposed to defend fuzzing attack. Different from the ordinary single feature detection model, the combined features by static and dynamic analysis to improve the defense ability are used. The experiment results show that the adversarial malware detection model with combined features can deal with the attack. The methods designed in this paper have great significance in improving the security of malware detection models and have good application prospects.