Abstract
Introduction
The integration of 5G and Software-Defined Networking (SDN) has introduced new security challenges due to limited processing capabilities, inefficient resource allocation, frequent user mobility, and the rapid growth of Internet of Things (IoT) devices. These dynamic network conditions increase the likelihood of malicious activities, highlighting the need for robust and intelligent intrusion detection techniques.
Methods
This research introduces a hybrid intrusion detection framework, termed APCHC-DDST-IV-CLSTM-ICWO, which integrates Affinity Propagated Cohesive Hierarchical Clustering, Deep Dual Stream SpaTemp Info-Varcoder, Dense Convolutional Long Short-Term Memory (CLSTM), and Improved Chaotic Walrus Optimization. The clustering module identifies attack patterns, while the dual-stream Info-Varcoder extracts representative spatial and temporal features. The Dense CLSTM captures sequential dependencies in network traffic to improve attack classification, and the Improved Chaotic Walrus Optimisation algorithm optimises the model parameters to enhance predictive performance. The proposed approach was validated using a 5G-SDN dataset generated with the MATLAB SDN Toolbox, with network traffic and attack scenarios simulated through the NS-3 network simulator.
Results
The model showed a superior classification performance with a high average accuracy of 0.985±0.002, precision of 0.984±0.003, and F1 score of 0.985±0.003. The model’s low log-loss of 0.411 allows for the accurate separation of attack categories, including unknown attacks. This method improves the security and performance of 5G-SDN by increasing the throughput to 3218 samples/second and the detection time to 1.74 seconds.
Discussion
The results of the experiment demonstrate that the proposed APCHC-DDST-IV-CLSTM-ICWO framework offers a feasible method to secure 5G-SDN networks. The system provides high detection accuracy, effective processing, and reliable identification of emerging cyber threats by combining hierarchical clustering, deep feature learning, sequence modeling, and optimization algorithms. These findings indicate that it is appropriate for improving the operational effectiveness and security of next-generation 5G-SDN configurations.