An Android malware detection method based on XGBoost machine learning algorithm

A malware and machine learning technology, applied in machine learning, instrumentation, computing and other directions, can solve the problems of increasing Android system attacks, low malware accuracy, and low classification accuracy, achieve good classification performance, and reduce the probability of attacks , the effect of high classification accuracy

Active Publication Date: 2019-03-29
GUANGDONG UNIV OF TECH
View PDF4 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the classification accuracy of this static detection method is not high, and the correct rate of malware detection is low, which increases the probability of the Android system being attacked due to detection errors.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • An Android malware detection method based on XGBoost machine learning algorithm
  • An Android malware detection method based on XGBoost machine learning algorithm
  • An Android malware detection method based on XGBoost machine learning algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0060] The present invention will be further described below in conjunction with specific embodiment:

[0061] A kind of Android malicious software detection method based on XGBoost machine learning algorithm described in the present embodiment, specific content is as follows:

[0062] XGBoost (eXtreme Gradient Boosting) is an integrated learning algorithm proposed by Tian Chen in 2015. In the XGBoost integrated learning framework, the main parameters that directly affect its classification performance are the parameter shrinkage (shrinkage) and the minimum sample weight threshold in the child nodes. (min_child_weight). Too small shrinkage will lead to overfitting of the algorithm, and larger shrinkage will cause the algorithm to fail to converge. For min_child_weight, too small will lead to overfitting of the algorithm, and too large mini_child_weight will lead to the classification performance of the algorithm for linearly inseparable data.

[0063] Therefore, in this embod...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to an Android malware detection method based on XGBoost machine learning algorithm. The method includes: extracting Permission Intent, Component and API call feature by decompiling the apk file, and composing a feature matrix quantitatively; using the parallelism and strong robustness of ant colony algorithm, optimizing the parameters of XGBoost classifier to obtain the optimal target and the optimal parameter combination of XGBoost. Compared with the traditional XGBoost algorithm, the improved XGBoost machine learning algorithm proposed by the invention, has higher classification accuracy during Android malware detection, improves the accuracy of malware detection, and reduces the probability of Android system attack due to detection errors.

Description

technical field [0001] The invention relates to the technical field of malware detection on an Android platform, in particular to an Android malware detection method based on an XGBoost machine learning algorithm. Background technique [0002] The Android system was officially released by Google on November 5, 2007. As an operating system based on the Linux kernel, its open source and free features make the Android system become the smart mobile device operating system with the largest market share at an extremely fast speed. system. However, while it is popular among App developers and users, it has also become the preferred target of malicious attackers. The rapid growth of Android malware has posed a serious threat to users' security and privacy. Malware steals users' private data, causing property losses, and exploiting system loopholes to obtain higher permissions and achieve greater harm. With the continuous advancement of the mobile payment industry, the Internet + ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/56G06N3/00G06N20/00
CPCG06F21/56G06N3/006Y02D10/00
Inventor 王雪敬凌捷孙玉孙宇平
Owner GUANGDONG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap