Optimized extreme learning machines with deep learning for high-performance network traffic classification

The exponential growth in network users and applications, coupled with increasing dependence on networked systems, has elevated network security to a paramount concern for service providers and organizations. Traffic analysis has emerged as a pivotal technique for identifying malicious activities, enabling critical functions such as bandwidth management, fault detection, quality assessment, pricing, and lawful security monitoring. We propose a novel framework for network traffic classification using an Improved Extreme Learning Machine (IELM). The proposed approach advances traditional extreme learning by incorporating a particle swarm optimization algorithm to optimize model parameters, alongside a deep learning-based feature selection mechanism to assess and prioritize input feature relevance, thereby enhancing classification precision. The framework's performance was rigorously evaluated using the CICIDS 2017 dataset, a widely recognized benchmark in network traffic analysis. The results demonstrate the framework's capability to accurately classify network traffic into secure and insecure categories, achieving a remarkable detection accuracy of 98.756%. These findings underscore the efficacy of the IELM-based approach in detecting malicious activities and mitigating security risks, offering a robust and scalable solution for strengthening network protection.