Privacy protection mobile malicious software detection method and system, storage medium and application

Abstract

The invention belongs to the technical field of mobile malicious software detection, and discloses a mobile malicious software detection method and system with privacy protection, a storage medium andan application. In a feature training phase, a semi-trusted part of a malicious software detection service provider MDSP operates software with known attributes in a simulator, a malicious feature set MS and a normal feature set NS are obtained by analyzing data during operation, and a corresponding effective Bloom filter and a corresponding effective Sketch data structure are established for detection and use; the mobile device collects runtime data of the position attribute software, encrypts the runtime data and sends the encrypted runtime data to the MDSP; the MDSP loads the Bloom filter,the Sketch and the received encrypted data to a trusted part of the MDSP and then executes a detection program; collecting software with newly confirmed attributes, adding the software into the training set, and updating MS and NS. Mobile malware detection is allowed to be outsourced to cloud in an efficient mobile user privacy protection mode to achieve malware detection serving as service, andthe method is suitable for malware detection algorithms matched with various features.

Description

technical field [0001] The invention belongs to the technical field of mobile malware detection, and in particular relates to a privacy protection mobile malware detection method, system, storage medium and application. Background technique [0002] At present, the closest existing technology: Human society has entered the era of mobile Internet, and the era of Internet of Things is coming. Various mobile smart devices play an indispensable role in people's daily life. The mobile operating system can run applications provided by any third party to serve people's daily life. Mobile applications also involve various aspects of user privacy (such as living habits, medical data, and even property-related account passwords). The openness of mobile malware, which seriously threatens people's privacy and even property safety, has also developed rapidly. [0003] Current malware detection methods mainly include static analysis and dynamic analysis. Static analysis mainly analyzes ...

AI Technical Summary

Problems solved by technology

[0012](1) Existing technologies seldom consider privacy protection in cloud-based mobile malware detection

[0013](2) Existing technologies that consider privacy protection do not take into account the performance overhead of the mobile terminal, so the mobile terminal still requires high performance overhead

[0014](3) Existing technologies that consider privacy protection use cryptography-based schemes, so the detection efficiency is generally relatively low

[0015] Difficulty in solving the above technical problems: Deploying mobile malware detection to the cloud can reduce the performance overhead of the mobile terminal, while cloud detection will expose users’ private data, but rarely There are related programs to protect user privacy

Most of the current privacy protection schemes are based on cryptography schemes, so they generally have the disadvantage of low efficiency

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