Improved forest algorithm-based Android malicious software detection method

Abstract

The invention discloses an improved forest algorithm-based Android malicious software detection method. The method comprises an S01 stage of obtaining a data set, an S02 stage of carrying out reverse processing, an S03 stage of extraction feature vectors, an S04 stage of optimizing the feature vectors, an S05 stage of generating a decision tree set, an S06 stage of calculating a decision tree weight, an S07 stage of generating a final classification result and an S08 stage of assessing the classification result. Compared with the traditional detection method, the improved forest algorithm-based Android malicious software detection method has higher classification precision, and has the effects of improving the correctness of malicious software detection and reducing the probability that Android systems are attacked due to detection errors.

Description

technical field [0001] The invention relates to the technical field of software security, in particular to an Android malware detection method based on an improved forest algorithm. Background technique [0002] The Android system is an open source operating system based on the Linux kernel. It has become the mobile device terminal platform with the largest market share, but its openness also makes it the largest development platform for malware; Software, relying solely on humans for detection and classification has long been outdated, and machine learning has become the mainstream. [0003] At present, many classification algorithms in machine learning are applied to Android malware detection. Zhang Yiting and others proposed a method based on naive Bayesian ( Bayes, NB)’s Android application malicious behavior identification method extracts whether the software has applied for too many permissions, whether there is a sensitive permission combination, etc. Rui and Yang...

AI Technical Summary

Problems solved by technology

Although the research of the above two people has realized the detection of Android malware, the detection accuracy is not high enough.

[0005] Yuan Z et al. implemented an online malware detection tool Droid Detector by using a deep learning algorithm, which realized online detection and analysis of Android applications, but its algorithm complexity is high, and it consumes a lot of computer memory.

Wen Weiping proposed a collaborative malicious code detection scheme based on mobile phone and server, and Hang Huan et al. designed a three-layer hybrid ensemble algorithm (THEA) to comprehensively evaluate the malicious behavior of Android applications. relatively complex

[0006] To sum up, the current detection research results using machine learning algorithms are relatively mature, but there are still many deficiencies in detection accuracy, detection efficiency, implementation complexity, etc., and the solution of these problems will inevitably require research and improvement of detection algorithms

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