Android Malware Detection Using a Modified Dwarf Mongoose Algorithm
Unknown authors
Abstract
The ubiquity of mobile devices due to the advancement of Internet technology has increased the threat of Android malware, which places users' data, identities, and finances at risk of theft and cybercriminal activities.Android malware detection (AMD), which uses machine learning, faces challenges with high-dimensional datasets, leading to increased computation and reduced accuracy.Feature selection methods extract key features and eliminate redundant data but are still susceptible to overlooking important features, leading to false positives or negatives.This study aims to enhance AMD via a modified dwarf mongoose optimization algorithm (DMOA) for efficient feature selection.The DMOA is improved with adaptive mechanisms, including crossover and mutation strategies, to explore the solution space better.These enhancements help the DMOA avoid local optima and identify superior feature subsets for more accurate malware detection.The CICAndMal2017 dataset is used to evaluate the effectiveness of different machine learning classifiers.The experimental results demonstrate that the proposed modified DMOA model achieves exceptional accuracy.The decision tree (DT), random forest (RF), and XGBoost models have 100% accuracy in binary classification.Furthermore, the proposed modified DMOA consistently outperforms several existing AMD modelsincluding Particle Swarm Optimization (PSO), Multi-Verse Optimizer (MVO), Enhanced Moth Flame Optimizer (EMFO), Aquila Optimizer, and Grey Wolf Optimizer (GWO)-in terms of accuracy, based on results reported in previous studies using the same dataset.