Overview of Generative AI Techniques for Cybersecurity

Abstract

Generative AI techniques have been popular since they can generate data or content that could be hardly distinguished from genuine ones. This chapter comprehensively reviews generative AI for cybersecurity and its definition, history, and applications in different fields. It covers basic ideas such as generative models, probability distributions, and latent spaces. Also, it goes into more detail on some of the more popular approaches like GANs, VAEs, and the combination of RL. The chapter explores the structure and training processes of GANs and VAEs and demonstrates their application in tasks such as image synthesis, data enhancement, and novelty detection. Also, it explores the interaction between RL and generative models and the challenges, including the exploration-exploitation trade-off. The chapter focuses on the development of generative AI with the help of DL and analyses the benefits of deep generative models and their usage in various fields. Evaluation measures and the problems with measuring generative models are discussed, focusing on the methods of improving the measurement accuracy. Finally, the chapter focuses on new directions, like transformer-based models and self-supervised learning, to look at the future of generative AI. The emphasis is made on understanding these techniques due to their versatility, and some ideas about the possible further developments of the findings for other fields and future studies and applications are provided.