Android malware category detection using a novel feature vector-based machine learning model
Abstract Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones. The researchers have conducted num...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-03-01
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| Series: | Cybersecurity |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42400-023-00139-y |
| _version_ | 1797864393429483520 |
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| author | Hashida Haidros Rahima Manzil S. Manohar Naik |
| author_facet | Hashida Haidros Rahima Manzil S. Manohar Naik |
| author_sort | Hashida Haidros Rahima Manzil |
| collection | DOAJ |
| description | Abstract Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones. The researchers have conducted numerous studies on the detection of Android malware, but the majority of the works are based on the detection of generic Android malware. The detection based on malware categories will provide more insights about the malicious patterns of the malware. Therefore, this paper presents a detection solution for different Android malware categories, including adware, banking, SMS malware, and riskware. In this paper, a novel Huffman encoding-based feature vector generation technique is proposed. The experiments have proved that this novel approach significantly improves the efficiency of the detection model. This method makes use of system call frequencies as features to extract malware’s dynamic behavior patterns. The proposed model was evaluated using machine learning and deep learning methods. The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%. |
| first_indexed | 2024-04-09T22:52:18Z |
| format | Article |
| id | doaj.art-eca085512873474cb197b1c0a292fa9e |
| institution | Directory Open Access Journal |
| issn | 2523-3246 |
| language | English |
| last_indexed | 2024-04-09T22:52:18Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cybersecurity |
| spelling | doaj.art-eca085512873474cb197b1c0a292fa9e2023-03-22T11:37:55ZengSpringerOpenCybersecurity2523-32462023-03-016111110.1186/s42400-023-00139-yAndroid malware category detection using a novel feature vector-based machine learning modelHashida Haidros Rahima Manzil0S. Manohar Naik1Department of Computer Science, Central University of KeralaDepartment of Computer Science, Central University of KeralaAbstract Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android smartphones. The researchers have conducted numerous studies on the detection of Android malware, but the majority of the works are based on the detection of generic Android malware. The detection based on malware categories will provide more insights about the malicious patterns of the malware. Therefore, this paper presents a detection solution for different Android malware categories, including adware, banking, SMS malware, and riskware. In this paper, a novel Huffman encoding-based feature vector generation technique is proposed. The experiments have proved that this novel approach significantly improves the efficiency of the detection model. This method makes use of system call frequencies as features to extract malware’s dynamic behavior patterns. The proposed model was evaluated using machine learning and deep learning methods. The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%.https://doi.org/10.1186/s42400-023-00139-yAndroid malwareDynamic analysisMalware categoryHuffman coding |
| spellingShingle | Hashida Haidros Rahima Manzil S. Manohar Naik Android malware category detection using a novel feature vector-based machine learning model Cybersecurity Android malware Dynamic analysis Malware category Huffman coding |
| title | Android malware category detection using a novel feature vector-based machine learning model |
| title_full | Android malware category detection using a novel feature vector-based machine learning model |
| title_fullStr | Android malware category detection using a novel feature vector-based machine learning model |
| title_full_unstemmed | Android malware category detection using a novel feature vector-based machine learning model |
| title_short | Android malware category detection using a novel feature vector-based machine learning model |
| title_sort | android malware category detection using a novel feature vector based machine learning model |
| topic | Android malware Dynamic analysis Malware category Huffman coding |
| url | https://doi.org/10.1186/s42400-023-00139-y |
| work_keys_str_mv | AT hashidahaidrosrahimamanzil androidmalwarecategorydetectionusinganovelfeaturevectorbasedmachinelearningmodel AT smanoharnaik androidmalwarecategorydetectionusinganovelfeaturevectorbasedmachinelearningmodel |