A Survey on Next Generation Intrusion Detection Systems Empowering Advanced Threat Detection with Generative AI

Abstract: Intrusion Detection Systems (IDSs) have been crucial in protecting computer networks from malicious activities. However, with the rapid evolution of cyber threats and the increasing complexity of network architectures, traditional IDSs are insufficient for effectively detecting and preventing modern attacks. Next Generation Intrusion Detection Systems (NG-IDSs) have emerged in response to these challenges, incorporating advanced technologies to enhance detection capabilities and improve overall security. This survey provides an overview highlighting the key features, applications in diverse networks, and discussing current challenges. It uniquely examines the integration of Generative AI (Gen AI) within IDS frameworks, focusing on Generative Adversarial Networks (GANs) to create synthetic data and emulate complex attack patterns, significantly enhancing the detection of previously unseen threats. Additionally, the survey explores the use of ChatGPT for real-time threat alerts and Large Language Models (LLMs) like GPT-4 in protecting critical infrastructures such as energy grids. This survey aims to offer valuable insights by identifying the challenges and limitations faced by NG-IDSs and proposing areas for future research and development. Keywords: AI, ChatGPT, IDS, intrusion detection system, Generative Adversarial Networks, Generative AI, models, datasets, IoT, security, social engineering, LLM