Comparative Analysis of CNNs and RNNs in Hybrid Deep Learning for Malware Classification

Abstract

With the rapid evolution of malware, advanced detection mechanisms are crucial for safeguarding digital infrastructures. This paper presents a comparative analysis of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) within hybrid deep learning frameworks for malware classification. By leveraging the feature extraction capabilities of CNNs and the temporal sequence modeling strengths of RNNs, hybrid architectures aim to enhance detection accuracy and robustness. The study evaluates standalone CNNs, RNNs, and hybrid CNN-RNN models using a benchmark malware dataset, focusing on metrics such as accuracy, precision, recall, and computational efficiency. Results demonstrate that hybrid models outperform their standalone counterparts by achieving higher classification accuracy and improved adaptability to diverse malware types. Furthermore, the paper explores the trade-offs in computational overhead and provides insights into the optimal configuration of CNN and RNN layers in hybrid systems. This work underscores the potential of integrating CNNs and RNNs to address the dynamic nature of malware threats and offers practical recommendations for deploying these systems in real-world cybersecurity applications.