| Anvarkhon Majidov A Fuzzy Neural Network Approach for Intelligent Environmental Monitoring and Anomaly Detection in IoT Networks |
|---|
| Abstract. The rapid deployment of Internet of Things (IoT) technologies has significantly expanded the capabilities of large-scale environmental monitoring systems. How-ever, the heterogeneity of sensor devices and the openness of communication channels increase the risk of anomalous data transmission and malicious interference. This paper proposes an intelligent environmental monitoring approach based on a fuzzy neural network for anomaly detection in IoT networks. The proposed method combines fuzzy logic and a multilayer neural network to analyze sensor data packets using nine network traffic features. Adaptive adjustment of membership functions during neural network training enables probabilistic evaluation of normal and anomalous behavior. The approach is integrated into a software-defined networking (SDN) architecture, allowing real-time traffic analysis, node validation, and dynamic synthesis of packet filtering rules. Simulation results demonstrate high anomaly detection accuracy with moderate processing overhead, confirming the effectiveness of the proposed solution for secure and scalable environmental monitoring. |
| Keywords: Environmental monitoring, Internet of Things, fuzzy logic, neural networks, anomaly detection, software-defined networking |
 Download PDF |
| DOI: https://doi.org/10.54381/itta2026.3.04 |