Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory
The widespread integration of smartphones into modern society has profoundly impacted various aspects of our lives, revolutionizing communication, work, entertainment, and access to information. Among the diverse range of smartphones available, those operating on the Android platform dominate the ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/3/482 |
| _version_ | 1797318897087545344 |
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| author | Rahul Gupta Kapil Sharma Ramesh Kumar Garg |
| author_facet | Rahul Gupta Kapil Sharma Ramesh Kumar Garg |
| author_sort | Rahul Gupta |
| collection | DOAJ |
| description | The widespread integration of smartphones into modern society has profoundly impacted various aspects of our lives, revolutionizing communication, work, entertainment, and access to information. Among the diverse range of smartphones available, those operating on the Android platform dominate the market as the most widely adopted type. With a commanding 70% share in the global mobile operating systems market, the Android OS has played a pivotal role in the surge of malware attacks targeting the Android ecosystem in recent years. This underscores the pressing need for innovative methods to detect Android malware. In this context, our study pioneers the application of rough set theory in Android malware detection. Adopting rough set theory offers distinct advantages, including its ability to effectively select attributes and handle qualitative and quantitative features. We utilize permissions, API calls, system commands, and opcodes in conjunction with rough set theory concepts to facilitate the identification of Android malware. By leveraging a Discernibility Matrix, we assign ranks to these diverse features and subsequently calculate their reducts–streamlined subsets of attributes that enhance overall detection effectiveness while minimizing complexity. Our approach encompasses deploying various Machine Learning (ML) algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbor, Random Forest, and Logistic Regression, for malware detection. The results of our experiments demonstrate an impressive overall accuracy of 97%, surpassing numerous state-of-the-art detection techniques proposed in existing literature. |
| first_indexed | 2024-03-08T03:59:03Z |
| format | Article |
| id | doaj.art-d4751500fd274fa990cbce65a3f8f1a9 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T03:59:03Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d4751500fd274fa990cbce65a3f8f1a92024-02-09T15:10:22ZengMDPI AGElectronics2079-92922024-01-0113348210.3390/electronics13030482Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set TheoryRahul Gupta0Kapil Sharma1Ramesh Kumar Garg2Department of Information Technology, Delhi Technological University, New Delhi 110042, IndiaDepartment of Information Technology, Delhi Technological University, New Delhi 110042, IndiaDepartment of Mechanical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal 131039, IndiaThe widespread integration of smartphones into modern society has profoundly impacted various aspects of our lives, revolutionizing communication, work, entertainment, and access to information. Among the diverse range of smartphones available, those operating on the Android platform dominate the market as the most widely adopted type. With a commanding 70% share in the global mobile operating systems market, the Android OS has played a pivotal role in the surge of malware attacks targeting the Android ecosystem in recent years. This underscores the pressing need for innovative methods to detect Android malware. In this context, our study pioneers the application of rough set theory in Android malware detection. Adopting rough set theory offers distinct advantages, including its ability to effectively select attributes and handle qualitative and quantitative features. We utilize permissions, API calls, system commands, and opcodes in conjunction with rough set theory concepts to facilitate the identification of Android malware. By leveraging a Discernibility Matrix, we assign ranks to these diverse features and subsequently calculate their reducts–streamlined subsets of attributes that enhance overall detection effectiveness while minimizing complexity. Our approach encompasses deploying various Machine Learning (ML) algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbor, Random Forest, and Logistic Regression, for malware detection. The results of our experiments demonstrate an impressive overall accuracy of 97%, surpassing numerous state-of-the-art detection techniques proposed in existing literature.https://www.mdpi.com/2079-9292/13/3/482androidmalwarerankingreductrough setsprediction |
| spellingShingle | Rahul Gupta Kapil Sharma Ramesh Kumar Garg Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory Electronics android malware ranking reduct rough sets prediction |
| title | Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory |
| title_full | Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory |
| title_fullStr | Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory |
| title_full_unstemmed | Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory |
| title_short | Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory |
| title_sort | innovative approach to android malware detection prioritizing critical features using rough set theory |
| topic | android malware ranking reduct rough sets prediction |
| url | https://www.mdpi.com/2079-9292/13/3/482 |
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