Enhanced Multiclass Android Malware Detection Using a Modified Dwarf Mongoose Algorithm
Rawan D. Alabdallat, Mosleh M. Abualhaj, Ahmad Adel Abu-Shareha
Abstract
The Android operating system has the most market share due to its easy handling and numerous advantages to Android users, which have attracted malicious actors. Android malware detection (AMD) systems based on machine learning (ML) are progressively being developed. However, these systems frequently struggle with high-dimensional datasets, increasing computation time, and lower accuracy. This study proposes a novel method for identifying malware in Android applications that employs a modified Dwarf Mongoose Optimization Algorithm (DMOA) for feature selection. The modified DMOA uses adaptive strategies, including crossover and mutation, to explore the search space more effectively, avoiding local optima and revealing higher-quality feature subsets that increase detection performance. The proposed modified DMOA model is trained and evaluated using the CICAndMal2017 dataset. The results show that it significantly outperforms existing techniques, achieving an accuracy of 100%.