AI-based Intent-Driven Secure System Designer

In this paper, we present the AI-based SecureWeaver, a system designed to enhance the generation of secure network architectures. Our approach utilizes an intentbased representation of network service requirements, annotated with security constraints, and employs Design Space Exploration (DSE) augmented with classical machine learning algorithms to optimize system design. The primary input to our system consists of network service requirements and security constraints, while the output is a secure and functional network topology. By integrating AI into the existing SecureWeaver framework, our primary contribution lies in demonstrating significant performance improvements through the application of five wellestablished AI algorithms. Our experimental results show that the AI-based SecureWeaver achieves substantial reductions in the number of iterations and the time required to design secure systems compared to the non-AI baseline. These improvements are validated across various corporate network cases, highlighting the practical benefits of our approach. This work provides insights into the effectiveness of AI algorithms in accelerating secure design.