MALICIOUS APPLICATION DETECTION USING STACKED ACLR - CH. Rohitha; DR.P. Praveen (Utilitas Mathematica, 2025)
With the increasing prevalence of mobile applications, security threats posed by malicious apps have escalated, leading to data breaches and privacy violations. Traditional signature-based detection methods struggle against evolving attack techniques. This project proposes an advanced Malicious Application Detection System using a stacked ACLR deep learning model (Artificial Neural Networks, Convolutional Neural Networks, Long Short-Term Memory, and Recurrent Neural Networks) to enhance detection accuracy. The system leverages deep learning for robust feature extraction from permissions, API calls, network activity, and metadata, providing real-time and adaptive classification of applications. By integrating CNNs for spatial pattern recognition, LSTMs for sequential analysis, and RNNs for behavioral modeling, the proposed system significantly outperforms conventional machine learning methods. The experimental results demonstrate higher accuracy, reduced false positives, and improved detection of zero-day attacks. This project contributes to an intelligent, scalable, and automated cybersecurity framework, strengthening protection against malicious mobile applications in dynamic digital environments..