Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA)
Many smart mobile devices, including smartphones, smart televisions, smart watches, and smart vacuums, have been powered by Android devices. Therefore, mobile devices have become the prime target for malware attacks due to their rapid development and utilization. Many security practitioners have ado...
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| Format: | Article |
| Language: | English |
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IOS Press
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43582/ http://umpir.ump.edu.my/id/eprint/43582/1/Android%20malware%20detection%20using%20PMCC.pdf |
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| author | Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Azlee, Zabidi Ferda, Ernawan Syifak, Izhar Hisham Mohd Faizal, Ab Razak |
| author_facet | Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Azlee, Zabidi Ferda, Ernawan Syifak, Izhar Hisham Mohd Faizal, Ab Razak |
| author_sort | Nur Khairani, Kamarudin |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Many smart mobile devices, including smartphones, smart televisions, smart watches, and smart vacuums, have been powered by Android devices. Therefore, mobile devices have become the prime target for malware attacks due to their rapid development and utilization. Many security practitioners have adopted different approaches to detect malware. However, its attacks continuously evolve and spread, and the number of attacks is still increasing. Hence, it is important to detect Android malware since it could expose a great threat to the users. However, in machine learning intelligence detection, too many insignificant features will decrease the percentage of the detection’s accuracy. Therefore, there is a need to discover the significant features in a minimal amount to assist with machine learning detection. Consequently, this study proposes the Pearson correlation coefficient (PMCC), a coefficient that measures the linear relationship between all features. Afterwards, this study adopts the heatmap method to visualize the PMCC value in the color of the heat version. For machine learning classification algorithms, we used a type of fuzzy logic called lattice reasoning. This experiment used real 3799 Android samples with 217 features and achieved the best accuracy rate of detection of more than 98% by using Unordered Fuzzy Rule Induction (FURIA). |
| first_indexed | 2025-11-15T03:52:19Z |
| format | Article |
| id | ump-43582 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:52:19Z |
| publishDate | 2023 |
| publisher | IOS Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-435822025-05-16T02:42:40Z http://umpir.ump.edu.my/id/eprint/43582/ Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Azlee, Zabidi Ferda, Ernawan Syifak, Izhar Hisham Mohd Faizal, Ab Razak QA75 Electronic computers. Computer science Many smart mobile devices, including smartphones, smart televisions, smart watches, and smart vacuums, have been powered by Android devices. Therefore, mobile devices have become the prime target for malware attacks due to their rapid development and utilization. Many security practitioners have adopted different approaches to detect malware. However, its attacks continuously evolve and spread, and the number of attacks is still increasing. Hence, it is important to detect Android malware since it could expose a great threat to the users. However, in machine learning intelligence detection, too many insignificant features will decrease the percentage of the detection’s accuracy. Therefore, there is a need to discover the significant features in a minimal amount to assist with machine learning detection. Consequently, this study proposes the Pearson correlation coefficient (PMCC), a coefficient that measures the linear relationship between all features. Afterwards, this study adopts the heatmap method to visualize the PMCC value in the color of the heat version. For machine learning classification algorithms, we used a type of fuzzy logic called lattice reasoning. This experiment used real 3799 Android samples with 217 features and achieved the best accuracy rate of detection of more than 98% by using Unordered Fuzzy Rule Induction (FURIA). IOS Press 2023-04-03 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/43582/1/Android%20malware%20detection%20using%20PMCC.pdf Nur Khairani, Kamarudin and Ahmad Firdaus, Zainal Abidin and Azlee, Zabidi and Ferda, Ernawan and Syifak, Izhar Hisham and Mohd Faizal, Ab Razak (2023) Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA). Journal of Intelligent & Fuzzy Systems, 44 (4). pp. 5601-5615. ISSN 1875-8967. (Published) https://doi.org/10.3233/JIFS-222612 https://doi.org/10.3233/JIFS-222612 |
| spellingShingle | QA75 Electronic computers. Computer science Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Azlee, Zabidi Ferda, Ernawan Syifak, Izhar Hisham Mohd Faizal, Ab Razak Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) |
| title | Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) |
| title_full | Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) |
| title_fullStr | Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) |
| title_full_unstemmed | Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) |
| title_short | Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) |
| title_sort | android malware detection using pmcc heatmap and fuzzy unordered rule induction algorithm (furia) |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/43582/ http://umpir.ump.edu.my/id/eprint/43582/ http://umpir.ump.edu.my/id/eprint/43582/ http://umpir.ump.edu.my/id/eprint/43582/1/Android%20malware%20detection%20using%20PMCC.pdf |