Deep Learning in Malware Classification and Behavior Analysis
Derek Mohammed, Helen MacLennan · Advances in computational intelligence and robotics book series · 2025
The rapid evolution of malware poses significant challenges for traditional signature-based detection methods, necessitating more intelligent and adaptive approaches. Deep learning, a subset of artificial intelligence, has emerged as a powerful tool in malware classification and behavior analysis. This chapter explores how deep learning techniques, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, are applied to detect, classify, and understand malware in real-time. We discuss various input representations including raw binaries, opcode sequences, and API call graphs, and analyze their impact on model performance. Emphasis is placed on the advantages of deep learning over classical machine learning in handling obfuscated and polymorphic malware. Furthermore, we examine challenges such as dataset imbalance, adversarial attacks, and explainability, and propose possible future directions to improve robustness and transparency in AI-driven malware analysis.