Implementation of Fuzzy Clustering and Fuzzy Neural Network in Edge-Based Server Environments for Enhanced Secured Cloud Services

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.