The increasing complexity of cyber threats in edge–cloud integrated infrastructures necessitates adaptive and intelligent security mechanisms that transcend the limitations of traditional machine learning approaches. This paper presents a novel hybrid security architecture that combines fuzzy clustering and fuzzy neural networks (FNN) to enhance intrusion detection in distributed edge computing environments connected to centralized cloud servers. The proposed system employs adaptive fuzzy clustering at the cloud layer to identify dynamic and previously unknown threat patterns. These patterns are further analyzed in real-time using an Adaptive Neuro-Fuzzy Inference System (ANFIS), which continuously updates and optimizes fuzzy rules for improved threat detection. Experimental results demonstrate that the proposed model outperforms conventional Artificial Neural Networks (ANN) and Support Vector Machines (SVM), achieving a detection accuracy of 94.6%, a detection rate of 96.1%, and a false positive rate of only 2.8%. Real-time simulations report an average response delay of 1.45 seconds while maintaining secure data transmission through AES-256 encryption and a tunneling protocol. These findings validate the effectiveness of hybrid fuzzy intelligent systems for proactive and scalable cybersecurity in mission-critical, high-risk cloud-edge environments. The proposed framework also shows potential for future integration with quantum fuzzy systems and block chain-based security architectures.
