Detecting Android Malware: Unleashing the Power of Machine Learning

As smartphones continue to dominate the digital landscape, the threat of Android malware looms large, compromising user privacy and security. This study delves into the realm of machine learning to tackle the ever evolving challenge of detecting Android malware, unleashing the power of advanced algorithms to enhance malware detection capabilities. A comprehensive dataset comprising a diverse range of benign and malicious applications is collected and analyzed. Meticulous data preprocessing and feature engineering techniques are employed to extract meaningful attributes that capture the intricate behaviors and characteristics of malware. The research focuses on employing a variety of machine learning algorithms, including logistic regression, KNN, random forest, decision trees and ensemble models to identify the most suitable algorithm that exhibits optimal performance in terms of accuracy, precision, recall, and F1_score.. Furthermore, the research evaluates the trained model on a separate testing dataset, meticulously measuring its effectiveness in real-world scenarios and analyze key evaluation metrics such as accuracy and the area under the receiver operating characteristic curve (ROC-AUC) to provide a comprehensive assessment of the model's performance. The outcomes of this study pave the way for enhanced cybersecurity measures, empowering users to safeguard their smartphones from the ever-present threats of malicious applications.