An Effective Machine and Deep learning Algorithms for Android Malware Classification and Detection

Abstract

The proliferation of Android malware has become a significant concern in the cyber security landscape. Traditional signature-based detection methods are no longer effective against the rapidly evolving malware threats. Machine Learning (ML) and Deep Learning (DL) algorithms have emerged as a promising solution for Android malware classification and detection. This study aims to investigate the effectiveness of various ML and DL algorithms for Android malware detection. This work analyses the performance of several algorithms, including Support Vector Machines (SVM), Random Forest (RF), and Recurrent Neural Network (RNN). From this analysis, reveals the DL algorithms, particularly RNN, outperform traditional ML algorithms in terms of accuracy, precision, and recall. This finding suggests that the DL algorithm and selects the features can provide an effective solution for Android malware classification and detection. The results of this study can be used to develop a robust and efficient Android malware detection system, which can help protect against the increasing threats of mobile malware. Overall, this study demonstrates the potential of ML and DL algorithms in detecting Android malware and provides insights into the development of effective detection systems.