Android malware detection method based on improved Bayesian algorithm

A Bayesian algorithm and malware technology, applied in the direction of platform integrity maintenance, etc., can solve problems such as high false positive rate and achieve the effect of improving efficiency

Inactive Publication Date: 2015-05-06
NANJING UNIV OF POSTS & TELECOMM
View PDF1 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Anomaly detection is different from misuse detection. It learns the behavior of known malware by applying data mining and machine learning algorithms to predict unknown or novel malware. Although anomaly detection can detect unknown malware, but It sometimes has a high false positive rate

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
  • Android malware detection method based on improved Bayesian algorithm
  • Android malware detection method based on improved Bayesian algorithm
  • Android malware detection method based on improved Bayesian algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0020] This method extracts permission request files from malicious software and benign software, and then counts the combination of every two permission request labels as a feature attribute, and then uses the concept of mutual information to extract feature attributes with the mutual information evaluation function. The basic task of feature extraction is to extract from many From the feature attributes, find out those feature attributes that have a significant impact on the classification, and select these features as the feature attributes of the classification. Then the chi-square test method is used to remove the influence of redundant permission request label combinations on malware detection, and finally the naive Bayesian algorithm is used to establish mathematical modeling and generate a detection model. The invention proposes a method for detecting Android malware based on the improved Bayesian algorithm, which is composed of three parts: data collection, data proces...

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 provides an Android malware detection method based on an improved Bayesian algorithm. The feature attributes of Android malicious programs and well-behaved programs are analyzed and classified through the improved Bayesian algorithm to realize the malware detection method based on the improved Bayesian algorithm. A judgment on whether software is malware is implemented from the aspect of permission application of applications. According to the method, a permission request label in an Android permission request mechanism is taken as a detection data source. The malware and well-behaved software are distinguished in a permission request label combination way, and a detection model is built by using the improved Bayesian algorithm. The improved Bayesian algorithm is characterized in that mutual independence among attributes of the data source is considered, and a naive Bayesian classifier is used for performing data modeling, so that the detection index is increased greatly, the detection accuracy is increased, and the false alarm rate is lowered.

Description

technical field [0001] The invention provides an Android malicious software detection method based on the improved Bayesian algorithm, which mainly solves the problem of how to detect malicious software in Android platform application software, and belongs to the field of mobile application security. Background technique [0002] With the rapid development of the mobile Internet, smart phones with mobile operating systems have become the mainstream of mobile terminal development. Smartphones are no longer traditional communication tools. Like handheld computers, smartphones already have an independent operating system, and users can install or uninstall applications provided by third-party developers such as QQ, Baidu Maps, and Mobile Security Guard. To expand the functions of mobile phones. Among them, the Android mobile terminal operating system based on the Linux kernel has developed the fastest, with a market share of 75% in the third quarter of 2012, and applications w...

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): G06F21/56
Inventor 张迎周居友道马凤娇滕庆亚徐曼青王子元
Owner NANJING UNIV OF POSTS & TELECOMM
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