scholarly journals SaaS: A situational awareness and analysis system for massive android malware detection

2019 ◽  
Vol 95 ◽  
pp. 548-559 ◽  
Author(s):  
Yaocheng Zhang ◽  
Wei Ren ◽  
Tianqing Zhu ◽  
Yi Ren
Keyword(s):  
Situational Awareness ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection ◽  
Analysis System

Runtime-Based Behavior Dynamic Analysis System for Android Malware Detection

2013 ◽  
Vol 756-759 ◽  
pp. 2220-2225 ◽  
Author(s):  
Luo Xu Min ◽  
Qing Hua Cao
Keyword(s):  
Dynamic Analysis ◽  
Private Information ◽  
Malware Detection ◽  
Third Party ◽  
Android Malware ◽  
Android Malware Detection ◽  
Malicious Behavior ◽  
Result Show ◽  
Android Market ◽  
Analysis System

The most serious threats for Android users is come from application, However, the market lack a mechanism to validate whether these applications are malware or not. So, malware maybe leak users private information, malicious deductions for send premium SMS, get root privilege of the Android system and so on. In the traditional method of malware detection, signature is the only basis. It is far enough. In this paper, we propose a runtime-based behavior dynamic analysis for Android malware detection. The new scheme can be implemented as a system. We analyze 350 applications come from third-party Android market, the result show that our system can effectively detect unknown malware and the malicious behavior of malware.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

Android malware detection with contrasting permission patterns

2014 ◽  
Vol 11 (8) ◽  
pp. 1-14 ◽  
Author(s):  
Ping Xiong ◽  
Xiaofeng Wang ◽  
Wenjia Niu ◽  
Tianqing Zhu ◽  
Gang Li
Keyword(s):  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection
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