Real-Time Network Packet Classification Exploiting Computer Vision Architectures

Forthcoming 6G/NextG networks highlight the need for advanced Artificial Intelligence (AI)-based security mechanisms to identify malicious activities and adapt to emerging threats. In this context, the integration of computer vision techniques into the cybersecurity field is promising due to their potential for sophisticated pattern recognition. In this paper we introduce a computationally efficient classification scheme acting directly on the raw packets collected at base stations and enforcing real-time conversion of packets into images. The innovative points of the proposed solution are the lightweight implementation, aligning well with the demands of future 6G networks, and the operation at network edge, enabling early threat identification as close as possible to the packet origin. We investigate the performance of this approach both in terms of F1-score and prediction time using state-of-the-art computer vision architectures and a customized Convolutional Neural Network (CNN) in an intrusion detection problem using a 5G dataset. Experimental results show the superiority of the CNN architecture over complex models. Across multiple packet window sizes
$N$
(i.e., 10, 50, 100 packets), the CNN consistently outperforms the other state-of-the-art computer vision models, achieving very high F1-scores (namely, 0.99593, 0.99860, 0.99895). A scalability analysis highlights a trade-off between CNN scalability and performance, where larger
$N$
values lead to increased prediction time. On the other hand, the other computer vision models exhibit better scalability, enabling an optimal model selection without trade-offs.