WRAPPER-BASED METAHEURISTIC OPTIMIZATION ALGORITHMS FOR ANDROID MALWARE DETECTION: A CORRELATIVE ANALYSIS OF FIREFLY, BAT & WHALE OPTIMIZATION
Santosh Jhansi K
Centurion University of Technology and Management, Odisha, India
Sujatha Chakravarthy
Centurion University of Technology and Management, Bhuvaneswar, India
Ravi Kiran Varma P
Maharaj Vijayaram Gajapathi Raj College of Engineering, Vizianagaram, India
Abstract
Recently, Android malware threats have been increasing at a rapid pace along with their usage and popularity. Different studies have elucidated the importance of analyzing the Android permissions pattern for the effective detection of Android malware. Optimization of android malware detection with high-dimensional permissions data is a bottleneck, which is a burning issue. In this study, nature-inspired wrapper-based metaheuristic algorithms such as firefly, bat, and whale optimization algorithms are investigated for the analysis of different Android permission patterns suitable for malware detection. Different machine learning classification algorithms, such as Linear Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and Extreme Learning Machine (ELM) are employed for evaluation. The Wrapper-Based Feature Selection using the Firefly Algorithm (WFSFA) outperformed the other algorithms in terms of feature reduction and recorded an improved classification accuracy of 95.28% when experimented with high-dimensional CICInvesAndMal2019 feature dataset with 4115 features.
Keywords:
Android Security, Malware Analysis, Static Analysis, Feature Selection, Android Malware Classification, Firefly Algorithm (FA), Bat Algorithm (BA), Whale Algorithm (WA).Full Text:
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