LiteNet: lightweight network traffic classification using explainable artificial intelligence and model compression

Background Network Traffic Classification (NTC) is a critical process in network management as it increases visibility to provide enhanced security, optimised resource allocation, and quality of service. However, the escalating volume of network traffic and real-time applications in modern networking is straining traditional processing on high-performance servers. To meet modern requirements for low latency and bandwidth efficiency, network management is increasingly moving computation closer to sources that generate the data, a paradigm referred to as edge computing. This shift relies on resource-constrained devices such as routers, switches, and embedded systems. To address these constraints, researchers proposed lightweight NTC models designed to operate effectively in such environments. However, while these methods offer greater efficiency, they often compromise performance, making the models less effective in practice. Furthermore, despite being lightweight, the throughput of current NTC models remains insufficient to keep up with the high bandwidth demands of modern networks, which continues to limit their practical deployment. Methods Therefore, this article proposes LiteNet, a lightweight model for real-time NTC in a resource-constrained environment to address the disparity between model complexity and edge device limitations. LiteNet adapts the InceptionV1 module that integrates four dedicated parallel branches. Additionally, we integrate DeepSHAP, an Explainable Artificial Intelligence (XAI) technique, to enhance explainability and reduce input features. Furthermore, we leverage model compression techniques, including semi-structured pruning and quantisation, to form a sparse LiteNet architecture that enables lightweight design and real-time classification. Results LiteNet demonstrated F1-scores exceeding 99% across two datasets, ISCXVPN2016 and MalayaNetwork_GT, while having 40% fewer parameters compared to the baseline method. Benchmarking results demonstrated that LiteNet outperformed the state-of-the-art method in terms of performance and efficiency, delivering up to 238% higher throughput and lower resource utilisation on an NVIDIA Jetson Nano. These outcomes demonstrate LiteNet’s strong balance between performance and efficiency, establishing it as a viable lightweight solution for NTC in resource-constrained settings.