Next-Generation Reservoir Computing (NG-RC) Machine Learning Model for Advanced Cybersecurity

Preventing and mitigating cyberattacks requires precise network anomaly detection. Due to cyber threats' dynamic and complexity, traditional anomaly detection systems need more flexible and intelligent methods. This paper offers an enhanced use of Next-Generation Reservoir Computing (NG-RC) with Nonlinear Vector Auto-regression (NVAR) for network traffic anomaly detection. NG-RC overcomes reservoir computing's constraints by removing random matrices and decreasing meta-parameters and training data. Our method trains the NG-RC model on a dataset of typical network activity to create a predictive model of traffic patterns, allowing it to detect security breaches. To quickly discover unexpected network traffic patterns, the NG-RC model's ability to learn and forecast complicated time-series data makes it ideal for monitoring. We validated the NG-RC model on several network situations with varying traffic sizes and kinds. We found that NG-RC detects abnormalities faster than standard models and with fewer false positives. The NG-RC model's computational efficiency allows real-time analysis and detection, critical in operational contexts where speed is vital. Continuous learning lets the system adapt to the network's changing patterns, assuring long-term performance. This study suggests a significant enhancement in network security by leveraging autonomous systems that can detect, forecast, and react to cyber-attacks in real-time.