ABSTRACT Intent‐based networking (IBN) is an advanced approach that combines artificial intelligence (AI) and advanced machine learning (ML) technologies with automation for advanced network management by adapting the network's functions to an organization's business objectives. The concept of IBN is transforming autonomous network management with regard to cybersecurity. It improves the detection, response, and prevention of threats by encoding security policies into automated network actions and configurations. IBN enables adaptive, proactive, and enduring protection in sophisticated and evolving cybersecurity environments, thereby reinforcing cybersecurity resilience. This work proposes an integrated approach that combines IBN with ML and reinforcement learning (RL) to effectively defend against TCP SYN‐based DDoS attacks. The ML model proposed in this research outputs a detection accuracy of 99.86%. Additionally, the RL‐based response mitigation strategy improves response time by 43% in comparison to traditional reactive security methods. In the architecture IBNS framework, it actively updates high‐level security intents into adaptive policies for near real time response to mitigate threats achieving less than 0.0008 false positive rate. Besides, the system is able to remain stable within the network while automating the response to the threats and enduring versatile attacks. This automated approach allows security to be aligned with operational objectives enabling proactive, adaptive, and multi‐dimensional security. These findings mark the advancement that intelligent autonomous systems can provide towards cybersecurity in future network infrastructures, showing benefits of enhancing resilience through self‐learning and intent‐driven mechanisms.
Sidhu et al. (Thu,) studied this question.