Autonomous Cyber-Defense for IoT-Edge Networks Using a Starfish Algorithm Optimized Bayesian Constitutive Neural Networks with Cross-Domain Zero Trust

This paper presents an advanced autonomous cyber-defense framework for IoT-edge networks, leveraging a novel integration of Starfish Algorithm-Optimized Bayesian Constitutive Neural Networks (BCNN) within Cross-Domain Zero Trust architecture. The system begins with input data sourced from two comprehensive and diverse datasets, IoT-24 and TON_IoT-v2, which reflect a wide range of IoT traffic patterns and attack scenarios. To enhance data quality, a Reverse Lognormal Kalman Filter (RLKF) is employed for pre-processing, effectively normalizing and denoising the input data. The cleaned data is then analyzed by BCNNs, which provide probabilistic modeling to account for uncertainties in cyber threat detection. To overcome the inherent limitations of BCNNs in parameter tuning, the Starfish Optimization Algorithm (SFOA) is used to optimize the model, significantly improving detection accuracy while minimizing computational demands—an essential feature for deployment in resource-constrained edge environments. Security is further reinforced through a Cross-Domain Zero Trust model, enabling continuous authentication and verification of data flows to prevent unauthorized access. The proposed technique attains 8.26%, 3.06%, and 3.00% higher accuracy, 3.48%, 3.13%, and 3.39% higher precision over state-of-the-art methods including QIFL, ZTA-IoT, and XAI-based detection systems. The proposed approach thus delivers a scalable, intelligent, and resilient solution for securing heterogeneous IoT-edge ecosystems.