Two Phase Static Analysis Technique for Android Malware Detection

2015 ◽  
Author(s):  
Priyadarshani M. Kate ◽  
Sunita V. Dhavale
Keyword(s):  
Static Analysis ◽  
Malware Detection ◽  
Two Phase ◽  
Android Malware ◽  
Android Malware Detection ◽  
Analysis Technique

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.

scholarly journals A static analysis approach for Android permission-based malware detection systems

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257968
Author(s):  
Juliza Mohamad Arif ◽  
Mohd Faizal Ab Razak ◽  
Suryanti Awang ◽  
Sharfah Ratibah Tuan Mat ◽  
Nor Syahidatul Nadiah Ismail ◽  
...  
Keyword(s):  
Static Analysis ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Random Forest Algorithm ◽  
Android Malware ◽  
Detection Systems ◽  
Android Malware Detection ◽  
Security Evaluations ◽  
Android Permission

The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic analysis, which monitors malware behaviour. While both perform security evaluations successfully, there is still room for improvement. The goal of this research is to examine the effectiveness of static analysis to detect Android malware by using permission-based features. This study proposes machine learning with different sets of classifiers was used to evaluate Android malware detection. The feature selection method in this study was applied to determine which features were most capable of distinguishing malware. A total of 5,000 Drebin malware samples and 5,000 Androzoo benign samples were utilised. The performances of the different sets of classifiers were then compared. The results indicated that with a TPR value of 91.6%, the Random Forest algorithm achieved the highest level of accuracy in malware detection.

scholarly journals What Static Analysis Can Utmost Offer for Android Malware Detection

2019 ◽  
Vol 48 (2) ◽  
pp. 235-240 ◽  
Author(s):  
Abdullah Talha Kabakus
Keyword(s):  
Static Analysis ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Experimental Result ◽  
Android Malware ◽  
Static And Dynamic Analysis ◽  
Analysis Techniques ◽  
Android Malware Detection ◽  
Learning Techniques ◽  
Detection Approach

Malicious applications are widespread for Android despite the taken serious actions by the operating system. Static and dynamic analysis techniques are utilized to detect malware by identifying the signatures of malicious applications by inspecting both the resources and behaviors of malware, respectively. In this study, what static analysis can utmost offer to detect malware in Android ecosystem is discussed and experimented on commonly used datasets in the literature by proposing a novel Android malware detection approach based on static analysis techniques. Some novel static analysis features which are proved to be effective in terms of detecting malware in Android ecosystem and are underestimated by the related work in the literature are introduced by proving their effectiveness in this study. The experimental result shows that the proposed Android malware detection approach is very effective in terms of detecting Android malware. Each feature used by the proposed approach is evaluated by using different types of machine learning techniques in order to highlight its impact on detecting malware and inform the digital investigators. The accuracy of the proposed static analysis approach is calculated as high as 0.987 for 10,865 applications.

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