SDN-Enabled Networks: A Deeper Learning-Based Echo State Neural Network for Cyber Threat Detection

Modern networks are becoming more intricate and interconnected, which has increased the demand for robust and effective methods of identifying cyberthreats. SDN, or software-defined networking, is one possible method for enhancing network control and administration. This paper introduces a new method for cyber threat detection in SDN-enabled networks using an Echo State Neural Network (ESN) that is based on deep learning. Echo State Networks are ideally suited for network traffic pattern analysis because of their remarkable capacity to capture the temporal fluctuations found in time-series data. Using cutting-edge deep learning techniques, the proposed Deep Learning-based Echo State Network (ESN) automatically extracts hierarchical features from unprocessed network data. This makes it possible for the model to identify intricate and nuanced patterns that point to potential cyberthreats. The main goal of this work is to integrate Deep Learning methods with the conventional Echo State Network architecture. The model can adapt to the complex properties of SDN settings with effectiveness thanks to this integration. Through training on historical network data, the model learns to discern normal behavior and then recognizes anomalies that may point to possible cyberthreats. The suggested method seeks to decrease the frequency of false positives while increasing the efficacy and precision of cyber threat identification. To demonstrate the Deep Learning-based ESN's effectiveness in identifying a variety of cyberthreats, it is tested on an actual SDN-enabled network dataset. The findings demonstrate how the suggested model outperforms traditional approaches in terms of reaction time and accuracy of detection.