Multi-malware hybrid detection method, system and device with privacy protection

Abstract

The invention belongs to the technical field of malicious software detection, and discloses a mixed detection method, system and device for multiple malicious software with privacy protection. The third party generates a public-private key pair according to the homomorphic encryption algorithm, and publishes the public key; the client collects the behavior data of the user group using the software, conducts preliminary calculations, encrypts with the third-party public key, adds it to the generated random number, and uploads the result to Server: The server uses the reputation evaluation algorithm to encrypt data according to the uploaded user group, utilizes the homomorphic nature and interacts with the third party to decrypt, completes the calculation of different software reputation values, and determines the software detection sequence according to the software reputation value; Call the API usage data obtained by the decompiled software APK from the client in order, and perform static detection on the software according to the static learning model. Public key, using homomorphic properties and dynamic learning model for real-time detection.

Description

technical field [0001] The invention belongs to the technical field of malicious software detection, and in particular relates to a privacy-protected multi-malware mixed detection method, system and device. Background technique [0002] At present, the closest existing technology: mobile malware refers to the existence of eavesdropping on user calls, stealing user information, destroying user data, unauthorized use of paid services, sending Spam information, push advertisements or fraudulent information, affect the operation of mobile terminals, endanger Internet network security and other malicious acts of application software. With the popularization of smartphones and the vigorous development of the mobile application industry, mobile malware has flooded into the application market in large numbers. Mobile malware often introduces security vulnerabilities into mobile devices, causing financial losses to users, and causing privacy leaks and other issues. Therefore, estab...

AI Technical Summary

Problems solved by technology

[0008] (1) There is a lack of solutions in the prior art that can effectively detect whether an application is malicious before installation and in real-time operation

[0009] (2) The existing technology does not consider the priority of detection. For popular and widely used software, in order to prevent damage caused by malicious behavior, it should be detected first whether it is malicious

[0010] (3) The existing technology does not consider the user's privacy protection in the cloud server malware detection process

[0012] The difficulty of the above-mentioned technical problems lies in determining an effective detection scheme before the application is installed and used and in real-time operation, how to determine the detection priority to reduce the damage caused by malicious applications, and how to protect the user's privacy during the entire detection process

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