Entropy-Based Feature Grouping in Machine Learning for Android Malware Classification

2020 ◽  
pp. 59-72
Author(s):  
Hyunseok Shim ◽  
Souhwan Jung
Keyword(s):  
Machine Learning ◽  
Android Malware ◽  
Malware Classification ◽  
Feature Grouping

scholarly journals Machine learning aided Android malware classification

2017 ◽  
Vol 61 ◽  
pp. 266-274 ◽  
Author(s):  
Nikola Milosevic ◽  
Ali Dehghantanha ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Machine Learning ◽  
Android Malware ◽  
Malware Classification

scholarly journals PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting

2020 ◽  
Vol 12 (4) ◽  
pp. 66 ◽  
Author(s):  
Ajit Kumar ◽  
Vinti Agarwal ◽  
Shishir Kumar Shandilya ◽  
Andrii Shalaginov ◽  
Saket Upadhyay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Research Output ◽  
Application Programming Interface ◽  
Classification Performance ◽  
End Users ◽  
Android Malware ◽  
Detection Techniques ◽  
Malware Classification ◽  
Threat Reporting

Android malware has become the topmost threat for the ubiquitous and useful Android ecosystem. Multiple solutions leveraging big data and machine-learning capabilities to detect Android malware are being constantly developed. Too often, these solutions are either limited to research output or remain isolated and incapable of reaching end users or malware researchers. An earlier work named PACE (Platform for Android Malware Classification and Performance Evaluation), was introduced as a unified solution to offer open and easy implementation access to several machine-learning-based Android malware detection techniques, that makes most of the research reproducible in this domain. The benefits of PACE are offered through three interfaces: Representational State Transfer (REST) Application Programming Interface (API), Web Interface, and Android Debug Bridge (ADB) interface. These multiple interfaces enable users with different expertise such as IT administrators, security practitioners, malware researchers, etc. to use their offered services. In this paper, we propose PACER (Platform for Android Malware Classification, Performance Evaluation, and Threat Reporting), which extends PACE by adding threat intelligence and reporting functionality for the end-user device through the ADB interface. A prototype of the proposed platform is introduced, and our vision is that it will help malware analysts and end users to tackle challenges and reduce the amount of manual work.

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.

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