AI-powered malware detection: a hybrid CNN-RNN model for real-time threat analysis

The rapid increase in malware attacks poses a significant threat to modern information systems. Traditional signature-based antivirus systems often fail to detect novel or obfuscated threats effectively. This research proposes an AI-powered malware detection model that operates in real-time, combining the strengths of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to enhance accuracy and contextual analysis. The hybrid model utilizes byte-level visual representations of executable files, where CNN extracts spatial features and RNN captures temporal dependencies across data sequences. The model was trained and evaluated on benchmark datasets including EMBER-2018 and CIC-MalMem-2022, showing superior performance in comparison to classical approaches such as SVM, Random Forest, and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN-RNN model achieved 98.6% accuracy, with high recall and low false positive rates, making it a reliable solution for real-time threat detection. This study provides a robust and scalable framework for malware detection using advanced deep learning techniques, contributing a technically sound approach to the field of cybersecurity.