A novel feature selection technique: Detection and classification of Android malware

Android operating system is not just the most commonly employed mobile operating system, but also the most lucrative target for cybercriminals due to its extensive user base. In light of this, the objective of this research is to uncover a few features that can significantly enhance the detection of...

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Main Authors: Sandeep Sharma, Prachi, Rita Chhikara, Kavita Khanna
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Egyptian Informatics Journal
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1110866525000118
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author Sandeep Sharma
Prachi
Rita Chhikara
Kavita Khanna
author_facet Sandeep Sharma
Prachi
Rita Chhikara
Kavita Khanna
author_sort Sandeep Sharma
collection DOAJ
description Android operating system is not just the most commonly employed mobile operating system, but also the most lucrative target for cybercriminals due to its extensive user base. In light of this, the objective of this research is to uncover a few features that can significantly enhance the detection of Android malware through utilization of feature engineering. This work introduces a novel approach to feature selection that can discover a promising subset of features for effective malware detection. The proposed technique, Multi-Wrapper Hybrid Feature Selection Technique (MWHFST), integrates wrapper-based feature selection techniques to address the limitations of individual wrapper-based feature selection methods. The research employs extensive experiments on the Kronodroid dataset, a comprehensive and large-scale dataset, to gauge how well the proposed technique identifies and classifies malicious Android applications. Experimental results using machine learning algorithms demonstrate that the technique proposed in this research effectively integrates the advantages of individual feature selection techniques and exhibits the potential to identify a brief set of pivotal features for detecting Android malware. The proposed approach successfully identifies and categorizes malicious Android applications, achieving an accuracy of 98.8 % and 88 %, respectively, using only 31 features. This approach surpasses existing methods by delivering comparable performance with a significantly reduced number of features compared to individual approaches.
format Article
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institution Kabale University
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publisher Elsevier
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series Egyptian Informatics Journal
spelling doaj-art-d79c4f840ce54449b4def90c2295630c2025-01-29T05:00:20ZengElsevierEgyptian Informatics Journal1110-86652025-03-0129100618A novel feature selection technique: Detection and classification of Android malwareSandeep Sharma0 Prachi1Rita Chhikara2Kavita Khanna3CSE Department The NorthCap University Sector 23a Gurugram IndiaCSE Department The NorthCap University Sector 23a Gurugram India; Corresponding author.CSE Department The NorthCap University Sector 23a Gurugram IndiaDirector (Admissions and Examinations) South Asian University Delhi IndiaAndroid operating system is not just the most commonly employed mobile operating system, but also the most lucrative target for cybercriminals due to its extensive user base. In light of this, the objective of this research is to uncover a few features that can significantly enhance the detection of Android malware through utilization of feature engineering. This work introduces a novel approach to feature selection that can discover a promising subset of features for effective malware detection. The proposed technique, Multi-Wrapper Hybrid Feature Selection Technique (MWHFST), integrates wrapper-based feature selection techniques to address the limitations of individual wrapper-based feature selection methods. The research employs extensive experiments on the Kronodroid dataset, a comprehensive and large-scale dataset, to gauge how well the proposed technique identifies and classifies malicious Android applications. Experimental results using machine learning algorithms demonstrate that the technique proposed in this research effectively integrates the advantages of individual feature selection techniques and exhibits the potential to identify a brief set of pivotal features for detecting Android malware. The proposed approach successfully identifies and categorizes malicious Android applications, achieving an accuracy of 98.8 % and 88 %, respectively, using only 31 features. This approach surpasses existing methods by delivering comparable performance with a significantly reduced number of features compared to individual approaches.http://www.sciencedirect.com/science/article/pii/S1110866525000118Android MalwareOptimizationFeature SelectionWrapper based techniqueMachine Learning
spellingShingle Sandeep Sharma
Prachi
Rita Chhikara
Kavita Khanna
A novel feature selection technique: Detection and classification of Android malware
Egyptian Informatics Journal
Android Malware
Optimization
Feature Selection
Wrapper based technique
Machine Learning
title A novel feature selection technique: Detection and classification of Android malware
title_full A novel feature selection technique: Detection and classification of Android malware
title_fullStr A novel feature selection technique: Detection and classification of Android malware
title_full_unstemmed A novel feature selection technique: Detection and classification of Android malware
title_short A novel feature selection technique: Detection and classification of Android malware
title_sort novel feature selection technique detection and classification of android malware
topic Android Malware
Optimization
Feature Selection
Wrapper based technique
Machine Learning
url http://www.sciencedirect.com/science/article/pii/S1110866525000118
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