A Comprehensive Study on Deep Learning and Artificial Intelligence for Malware Analysis
Tukkappa K. Gundoor, Sridevi · 2025
This chapter offers a thorough review of the use of deep learning and artificial intelligence (AI) in malware analysis, emphasizing the essential methods, uses, difficulties, and prospects. It explains that essential ideas for feature extraction and malware classification, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), are examples of neural networks. The categorization of malware using supervised learning techniques like CNNs and RNNs as well as the identification of anomalies using unsupervised learning methods like auto encoders and GANs are also covered in this chapter. It solves issues such as the lack of labeled malware samples and the constantly changing nature of malware. The necessity of interpretable deep learning models to comprehend identified malware is also emphasized in this chapter. Deep learning and AI present significant opportunities for improving malware detection, classification, and dynamic analysis, ultimately enhancing the security of computer systems and networks against the risks posed by harmful software.