Publication: A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps
Date
2024
Authors
Chimeleze C.
Jamil N.
Alturki N.
Muhammad Zain Z.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier B.V.
Abstract
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
Description
Keywords
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