Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cyber Security

The rapid growth of Android devices and applications has significantly increased the risk of malware attacks, making mobile security a critical concern in today’s digital landscape. Traditional signature-based malware detection techniques are often ineffective against newly emerging and evolving threats, as they rely on known patterns and fail to detect zero-day attacks. This project proposes an Automated Android Malware Detection System using an Optimal Ensemble Learning Approach to enhance detection accuracy and robustness in cybersecurity applications. The system aims to identify malicious applications by analyzing their behavioral and static features using advanced machine learning techniques. The proposed methodology involves collecting Android application datasets and extracting relevant features such as permissions, API calls, and system behaviors. Data preprocessing techniques are applied to remove noise and handle missing values, followed by feature selection methods to identify the most significant attributes influencing malware detection. The system employs an ensemble learning approach, combining multiple machine learning models such as Random Forest, Support Vector Machines, Gradient Boosting, and Neural Networks. The optimal ensemble model is created by selecting and combining classifiers based on performance metrics, thereby improving overall prediction accuracy and reducing false positives. The performance of the system is evaluated using metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the ensemble learning approach outperforms individual models by providing higher detection accuracy and better generalization. The system is capable of detecting both known and unknown malware, making it suitable for real-time cybersecurity applications. Additionally, the automated nature of the system reduces human intervention and enhances scalability. Overall, this project highlights the effectiveness of ensemble learning techniques in strengthening Android malware detection and improving mobile security against evolving cyber threats.