![]() A hybrid Artificial Neuronal Classifier (ANC) is proposed for improving the classification of android malware. The nature-inspired wrapper-based algorithms are evaluated using well-known Machine Learning (ML) classifiers such as Linear Regression (LR), Decision Tree (DT), Random Forest (RF), K–Nearest Neighbor (KNN) & Support Vector Machine (SVM). Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential features. ![]() The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. The malware analysis with reduced feature space helps for the efficient identification of malware. ![]() Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats.
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