A Distributed Parallel Network Intrusion Detection System Based on Ray Framework With GPU Acceleration
Year: 2025
Type: article
Abstract: ABSTRACT In the era of the Internet of Things and big data, the training of machine learning models has become increasingly demanding due to the vast amounts of data involved. Reducing training time and improving classification accuracy are essential. This article proposes a high‐performance attack detection model (AE‐XGBoost) based on the distributed data parallel processing framework‐Ray. First, a solution called Dynamic Resource Adjustment for Model Training enhances training speed by dynamically adjusting resources, preventing resource idleness or overload, and ensuring optimal resource utilization at each stage. Second, the Dual‐Link Loss Autoencoder algorithm is employed for feature mining, improving anomaly detection and enabling clear visualization of normal and anomalous data. Finally, the data parallel XGBoost method is applied for attack classification. Experimental results on five public large‐scale datasets demonstrate that the proposed model outperforms several well‐established benchmark classification models in both performance and accuracy.
DOI: 10.1002/cpe.70021
Authors: Wenbin Yao, Longcan Hu, Yingying Hou
Institution: Beijing University of Posts and Telecommunications
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409266314