Adaptive Security Monitoring in Distributed Telecommunication Networks Using Neural Networks

Distributed telecommunication networks face increasing security challenges due to their scale and dynamic nature. To address these challenges, this study proposes a monitoring framework that leverages a backpropagation (BP) neural network for anomaly detection and adaptive security reconfiguration. It aims to enhance network resilience by enabling real-time detection, proactive monitoring, and dynamic adjustment of security parameters. The study presents the implementation of the framework using real-time traffic analysis, supervised learning, and contract-based policy monitoring. As a result, the system demonstrated high detection accuracy for various types of attacks, with notably low false positive rates. The adaptive configuration module also responded efficiently to changing network conditions by automatically adjusting security settings. Consequently, the study confirmed that combining BP neural networks with adaptive control mechanisms significantly improves the flexibility and robustness of distributed security monitoring. The benefits of this approach include increased detection reliability, faster incident response, and reduced resources overhead, making it suitable for real-world implementation.