Swarm Optimization and Machine Learning for Android Malware Detection

Abstract

Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. 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 nature-inspired wrapperbased 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). A hybrid Artificial Neuronal Classifier (ANC) is proposed for improving the classification of android malware. The experimental results yielded an accuracy of 98.87% with just seven features out of hundred API call features, i.e., a massive 93% of data optimization.

Abnormal Traffic Detection for Internet of Things Based on an Improved Residual Network

“…Diwan et al [12] proposed a novel, lightweight feature selection method for IoT intrusion detection, which leverages rank-based chi-square, Pearson correlation, and score correlation to identify key dataset features. Similarly, Jhansi et al [13] used Ant Lion Optimization, Cuckoo Search Optimization, and Firefly Optimization alongside autoencoders for efficient Application Programming Interface (API) scheduling in malware detection.…”