Self-Healing Cybersecurity Framework using Generative Artificial Intelligence for Autonomous Network Recovery

Abstract

Traditional cybersecurity technologies have difficulty adapting to the increasing sophistication and numbers of cyberattacks. Typically, traditional technologies use manual interventions and static rules to defend against cyberattacks; therefore, they are limited in their ability to adapt and respond quickly to changing and evolving threats. This research addresses this problem by proposing a Generative Artificial Intelligence (AI) Self-Healing Cybersecurity Framework to facilitate the autonomous detection of threats and recovery from attacks on networks. The proposed framework combines advanced machine learning methods, such as deep learning models, generative adversarial networks (GANs), and reinforcement learning, to create a smart, flexible, and adaptive cybersecurity architecture. This system monitors all your network traffic constantly for unusual activity, uses artificial intelligence technology to identify trends and forecast future threats, and creates simulations of potential attacks through a generative artificial intelligence module to help the system identify new types of threats before they become real. When a threat is identified, the decision engine automatically determines the best way to mitigate the threat without human intervention. Finally, the self-healing recovery process restores normal operation of the network. Based on an experimental evaluation with benchmark cybersecurity datasets, the proposed framework demonstrates a 97.1% detection accuracy (with 96.5% precision and 95.8% recall), significantly outperforming traditional intrusion detection systems. The autonomous recovery mechanism also reduces the time required to recover an entire network by over 70%, contributing to a greater level of resilience within the system. As such, the proposed framework provides a viable and scalable solution to improving cybersecurity within today's network environments.