Impact of Feature Selection Algorithms in Detecting Android Malware Using Machine Learning Over Permissions and API’s

Abstract

In recent times, an upsurge of highly sophisticated and intricate malware has emerged, becoming one of the most insidious and perilous attack techniques targeting critical information technology infrastructures. Android, the widely anticipated and open-source smartphone operating system has experienced exponential growth. However, this progress has been impeded by the escalating threat of Android malware, which exploits smartphones to carry out malicious acts. Malware employs a plethora of techniques to circumvent detection systems, presenting novel obstacles to reliable detection. Detecting Android malware efficiently and accurately is crucial in ensuring the security of Android OS users. Machine learning techniques have been widely employed to address this problem, and feature selection algorithms have been introduced to enhance the detection process. This paper investigates the impact of feature selection algorithms specifically applied to permission and API method information in detecting Android malware using different machine learning algorithms. Experiments were conducted to compare the performance of feature selection algorithms, focusing on Principal Component Analysis (PCA) feature selection, F-Score, Recursive feature selection, and Stochastic Neighbor Embedding (SNE). The results demonstrate the effectiveness of the PCA algorithm-based approach in selecting relevant features for malware detection, showing advantages over all feature selection algorithms and reducing the model-building time significantly. The findings highlight the importance of feature selection in optimizing the machine learning-based malware detection system. By selecting pertinent features, the detection process becomes more efficient, improving both accuracy and speed. The PCA algorithm-based feature selection approach outperformed the Feature selection method, showcasing its ability to effectively identify features relevant to Android malware detection.