Optimized Echo State Network-Based Deep Learning Model for Intrusion Detection in Wireless Communication

With the rapid expansion of wireless networks, the need for robust security measures is critical due to the rise in network intrusions and security threats. This paper introduces a novel intrusion detection system (IDS) based on Echo State Networks (ESNs), a type of reservoir computing model known for its training efficiency and ability to process temporal patterns. Our proposed ESN-based IDS is designed to detect and classify network intrusions with high accuracy while maintaining low computational costs. By training the model on datasets containing both normal and malicious network activity, we ensure its robustness against various attack types. Simulation and real-world experiments demonstrate that the ESN-based IDS outperform traditional machine learning methods in terms of detection accuracy, false positives, and response time. The lightweight nature of ESNs makes them suitable for resource-constrained environments, making this approach an effective solution for securing wireless networks.