A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps
The growing complexity of cyber threats has shifted the focus from merely identifying threats to detecting their origins, resulting in stronger defenses against malware. Traditional detection techniques are often inadequate against increasingly sophisticated malware, prompting this research article...
Saved in:
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Elsevier B.V.
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1833413218508210176 |
|---|---|
| author | Chimeleze C. Jamil N. Alturki N. Muhammad Zain Z. |
| author2 | 57222127806 |
| author_facet | 57222127806 Chimeleze C. Jamil N. Alturki N. Muhammad Zain Z. |
| author_sort | Chimeleze C. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The growing complexity of cyber threats has shifted the focus from merely identifying threats to detecting their origins, resulting in stronger defenses against malware. Traditional detection techniques are often inadequate against increasingly sophisticated malware, prompting this research article to propose a new clustering method?fuzzy C-mean simulated annealing (FCMSA)?to enhance malware detection through machine learning. The FCMSA clustering technique improves performance by minimizing vulnerabilities, reducing outliers, and optimizing large datasets. The proposed technique selects high-quality clusters from Android app permissions and, using lightGBM, classifies Android malware. Experimental results show that the proposed FCMSA-GBM technique achieves superior accuracy (99.21%) and precision (99.70%) compared to other prevalent cluster-based Android malware detection techniques, while also lowering error rates and execution time. ? 2024 |
| format | Article |
| id | my.uniten.dspace-36082 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | my.uniten.dspace-360822025-03-03T15:41:21Z A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps Chimeleze C. Jamil N. Alturki N. Muhammad Zain Z. 57222127806 36682671900 57226667238 59062250200 Simulated annealing Android apps Android malware Android malware detection C-means Clusterings Fuzzy C-Means clustering Gradient boosting Gradient boosting machine Malware detection Malwares Android malware The growing complexity of cyber threats has shifted the focus from merely identifying threats to detecting their origins, resulting in stronger defenses against malware. Traditional detection techniques are often inadequate against increasingly sophisticated malware, prompting this research article to propose a new clustering method?fuzzy C-mean simulated annealing (FCMSA)?to enhance malware detection through machine learning. The FCMSA clustering technique improves performance by minimizing vulnerabilities, reducing outliers, and optimizing large datasets. The proposed technique selects high-quality clusters from Android app permissions and, using lightGBM, classifies Android malware. Experimental results show that the proposed FCMSA-GBM technique achieves superior accuracy (99.21%) and precision (99.70%) compared to other prevalent cluster-based Android malware detection techniques, while also lowering error rates and execution time. ? 2024 Final 2025-03-03T07:41:21Z 2025-03-03T07:41:21Z 2024 Article 10.1016/j.eij.2024.100560 2-s2.0-85206613550 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206613550&doi=10.1016%2fj.eij.2024.100560&partnerID=40&md5=4127e4f38d77aca50dee8c76f32f1b94 https://irepository.uniten.edu.my/handle/123456789/36082 28 100560 Elsevier B.V. Scopus |
| spellingShingle | Simulated annealing Android apps Android malware Android malware detection C-means Clusterings Fuzzy C-Means clustering Gradient boosting Gradient boosting machine Malware detection Malwares Android malware Chimeleze C. Jamil N. Alturki N. Muhammad Zain Z. A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps |
| title | A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps |
| title_full | A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps |
| title_fullStr | A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps |
| title_full_unstemmed | A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps |
| title_short | A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps |
| title_sort | lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in android apps |
| topic | Simulated annealing Android apps Android malware Android malware detection C-means Clusterings Fuzzy C-Means clustering Gradient boosting Gradient boosting machine Malware detection Malwares Android malware |
| url_provider | http://dspace.uniten.edu.my/ |