Android traffic malware analysis and detection using ensemble classifier
This paper introduces the Systematic mAlware detection in android (STAR) technique designed to enhance accuracy in identifying and classifying Android malware, addressing significant concerns regarding device security and data privacy. The STAR method involves comprehensive data collection from dive...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S209044792400515X |
| _version_ | 1826929245469802496 |
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| author | A. Mohanraj K. Sivasankari |
| author_facet | A. Mohanraj K. Sivasankari |
| author_sort | A. Mohanraj |
| collection | DOAJ |
| description | This paper introduces the Systematic mAlware detection in android (STAR) technique designed to enhance accuracy in identifying and classifying Android malware, addressing significant concerns regarding device security and data privacy. The STAR method involves comprehensive data collection from diverse datasets, rigorous preprocessing for data quality improvement, and feature extraction using Principal Component Analysis (PCA). Butterfly optimization ensures selection of pertinent features, while ensemble classifiers including Bagging, AdaBoost, and LogitBoost are employed for robust model creation. Final classification is achieved via majority voting. Experimental validation demonstrates that STAR outperforms existing techniques such as ERBE, De-LADY, and MSFDROID, achieving detection rates 4.34 %, 1.41 %, and 2.52 % higher respectively. This innovative approach underscores its potential in mitigating the evolving threat landscape of Android malware, offering a promising avenue for enhancing mobile app security. |
| first_indexed | 2025-02-17T16:04:10Z |
| format | Article |
| id | doaj.art-3824bbd6567941ed975bbb7b2a2b75f8 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2025-02-17T16:04:10Z |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-3824bbd6567941ed975bbb7b2a2b75f82024-12-18T08:48:29ZengElsevierAin Shams Engineering Journal2090-44792024-12-011512103134Android traffic malware analysis and detection using ensemble classifierA. Mohanraj0K. Sivasankari1Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu 641202 India; Corresponding author.Department of Electronics and Communication Engineering, Akshaya College of Engineering and Technology, Bhagavathipalayam, Kinathukadavu, Coimbatore, Tamil Nadu 642109 IndiaThis paper introduces the Systematic mAlware detection in android (STAR) technique designed to enhance accuracy in identifying and classifying Android malware, addressing significant concerns regarding device security and data privacy. The STAR method involves comprehensive data collection from diverse datasets, rigorous preprocessing for data quality improvement, and feature extraction using Principal Component Analysis (PCA). Butterfly optimization ensures selection of pertinent features, while ensemble classifiers including Bagging, AdaBoost, and LogitBoost are employed for robust model creation. Final classification is achieved via majority voting. Experimental validation demonstrates that STAR outperforms existing techniques such as ERBE, De-LADY, and MSFDROID, achieving detection rates 4.34 %, 1.41 %, and 2.52 % higher respectively. This innovative approach underscores its potential in mitigating the evolving threat landscape of Android malware, offering a promising avenue for enhancing mobile app security.http://www.sciencedirect.com/science/article/pii/S209044792400515XMalware detectionMachine learningMalware variantsMalware Classifications |
| spellingShingle | A. Mohanraj K. Sivasankari Android traffic malware analysis and detection using ensemble classifier Ain Shams Engineering Journal Malware detection Machine learning Malware variants Malware Classifications |
| title | Android traffic malware analysis and detection using ensemble classifier |
| title_full | Android traffic malware analysis and detection using ensemble classifier |
| title_fullStr | Android traffic malware analysis and detection using ensemble classifier |
| title_full_unstemmed | Android traffic malware analysis and detection using ensemble classifier |
| title_short | Android traffic malware analysis and detection using ensemble classifier |
| title_sort | android traffic malware analysis and detection using ensemble classifier |
| topic | Malware detection Machine learning Malware variants Malware Classifications |
| url | http://www.sciencedirect.com/science/article/pii/S209044792400515X |
| work_keys_str_mv | AT amohanraj androidtrafficmalwareanalysisanddetectionusingensembleclassifier AT ksivasankari androidtrafficmalwareanalysisanddetectionusingensembleclassifier |