scholarly journals DATDroid: Dynamic Analysis Technique in Android Malware Detection

2020 ◽  
Vol 10 (2) ◽  
pp. 536
Author(s):  
Rajan Thangaveloo ◽  
Wong Wang Jing ◽  
Chiew Kang Leng ◽  
Johari Abdullah
Keyword(s):  
Dynamic Analysis ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection ◽  
Analysis Technique

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.

scholarly journals A Family of Droids-Android Malware Detection via Behavioral Modeling: Static vs Dynamic Analysis

2018 ◽  
Author(s):  
Lucky Onwuzurike ◽  
Mario Almeida ◽  
Enrico Mariconti ◽  
Jeremy Blackburn ◽  
Gianluca Stringhini ◽  
...  
Keyword(s):  
Dynamic Analysis ◽  
Malware Detection ◽  
Behavioral Modeling ◽  
Android Malware ◽  
Android Malware Detection

scholarly journals Runtime Detection Framework for Android Malware

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
TaeGuen Kim ◽  
BooJoong Kang ◽  
Eul Gyu Im
Keyword(s):  
Dynamic Analysis ◽  
Static Analysis ◽  
Suffix Tree ◽  
Malware Detection ◽  
Application Programming Interface ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Dynamic Features ◽  
Android Malware ◽  
Android Malware Detection

As the number of Android malware has been increased rapidly over the years, various malware detection methods have been proposed so far. Existing methods can be classified into two categories: static analysis-based methods and dynamic analysis-based methods. Both approaches have some limitations: static analysis-based methods are relatively easy to be avoided through transformation techniques such as junk instruction insertions, code reordering, and so on. However, dynamic analysis-based methods also have some limitations that analysis overheads are relatively high and kernel modification might be required to extract dynamic features. In this paper, we propose a dynamic analysis framework for Android malware detection that overcomes the aforementioned shortcomings. The framework uses a suffix tree that contains API (Application Programming Interface) subtraces and their probabilistic confidence values that are generated using HMMs (Hidden Markov Model) to reduce the malware detection overhead, and we designed the framework with the client-server architecture since the suffix tree is infeasible to be deployed in mobile devices. In addition, an application rewriting technique is used to trace API invocations without any modifications in the Android kernel. In our experiments, we measured the detection accuracy and the computational overheads to evaluate its effectiveness and efficiency of the proposed framework.

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