Work

A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps

Year: 2024

Type: article

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 .

Source: Egyptian Informatics Journal

DOI: 10.1016/j.eij.2024.100560

Authors: Collins Chimeleze, Norziana Jamil, Nazik Alturki, Zuhaira Muhammad Zain

Institutions: Universiti Tenaga Nasional, United Arab Emirates University, Princess Nourah bint Abdulrahman University

Language: English

Topic: Advanced Malware Detection Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,200