Hybrid Deep Learning Approach Based on LSTM and CNN for Malware Detection

Malware analysis is essential for detecting and mitigating the effects of malicious software. This study introduces a novel hybrid approach using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) to enhance malware analysis. The proposed work uses a malware classification method combining image processing and machine learning. Malware binaries are converted into grayscale images and analyzed with CNN-LSTM networks. Dynamic features are extracted, ranked, and reduced via Principal Component Analysis (PCA). Various classifiers are used, with final classification by a voting scheme, providing a robust solution for accurate malware family classification. Our approach processes binary code inputs, with the LSTM capturing temporal dependencies and the CNN performing parallel feature extraction. PCA is employed for prominent feature selection, reducing computational time. The proposed approach was evaluated on a public malware dataset and captured through network traffic, demonstrating state-of-the-art performance in identifying various malware families. It significantly reduces the resources required for manual analysis and improves system security. Our approach achieved high precision, recall, accuracy, and F1 score, outperforming existing methods. Future research directions include improving feature extraction techniques and developing real-time detection models that offer a powerful malware detection and analysis tool with promising results and potential for further advancements.