SemiDroid: a behavioral malware detector based on unsupervised machine learning techniques using feature selection approaches

With the exponential growth in Android apps, Android based devices are becoming victims of target attackers in the “silent battle” of cybernetics. To protect Android based devices from malware has become more complex and crucial for academicians and researchers. The main vulnerability lies in the un...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics Vol. 12; no. 5; pp. 1369 - 1411
Main Authors: Mahindru, Arvind, Sangal, A. L.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Clustering
Complex Systems
Computational Intelligence
Computer viruses
Control
Cybernetics
Datasets
Empirical analysis
Engineering
Feature selection
Linux
Machine learning
Malware
Mechatronics
Operating systems
Original Article
Pattern Recognition
Robotics
Smartphones
Systems Biology
Unsupervised learning
Feature selection
Intrusion detection
Permissions model
Unsupervised
Android apps
Cyber security
Smartphone
API calls
ISSN:1868-8071, 1868-808X
Online Access:Get full text
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Summary:With the exponential growth in Android apps, Android based devices are becoming victims of target attackers in the “silent battle” of cybernetics. To protect Android based devices from malware has become more complex and crucial for academicians and researchers. The main vulnerability lies in the underlying permission model of Android apps. Android apps demand permission or permission sets at the time of their installation. In this study, we consider permission and API calls as features that help in developing a model for malware detection. To select appropriate features or feature sets from thirty different categories of Android apps, we implemented ten distinct feature selection approaches. With the help of selected feature sets we developed distinct models by using five different unsupervised machine learning algorithms. We conduct an experiment on 5,00,000 distinct Android apps which belongs to thirty distinct categories. Empirical results reveals that the model build by considering rough set analysis as a feature selection approach, and farthest first as a machine learning algorithm achieved the highest detection rate of 98.8% to detect malware from real-world apps.
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-020-01238-9