Malicious software family classification avoidance method based on deep reinforcement learning

Abstract

The invention discloses a malicious software family classification avoidance method based on deep reinforcement learning, which adopts a reinforcement learning algorithm to create an environment and construct an intelligent agent, and modifies malicious software to be tested by continuously interacting with a target malicious software classifier to finally achieve the purpose of classification avoidance. Compared with other malicious software detection/classification confrontation methods, the method provided by the invention is easier to implement and lower in overhead; according to the malicious software family classification avoidance method based on deep reinforcement learning, malicious software can be effectively modified for various malicious software classification models constructed by using different features, meanwhile, the maliciousness function of the malicious software cannot be damaged, and finally, malicious software family classification is avoided; and finally, the robustness of the malicious software detection and classification model based on the machine learning algorithm is improved, and the defense capability of the antivirus engine is improved.

Description

technical field [0001] The invention relates to the technical fields of software security and information system security, in particular to a method for classifying and evading malicious software families based on deep reinforcement learning. Background technique [0002] Malicious software refers to software that realizes the attacker's malicious purpose. Whether the malicious purpose exists is the basis for judging whether the software is malicious software. Malicious software can deliberately delete some files or directory information to destroy computer hard disk devices, steal computer user information and privacy without the user's knowledge, and illegally obtain control of computer system and network resources, destroying the reliability of computers and networks. reliability, integrity and availability. With the continuous development of the new generation of network information technology, more and more people start to use the Internet, and the Internet begins to a...

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The model training process involved in the above two methods

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