Protecting NextG Military Networks with Convolutional Neural Networks

Nowadays, defense applications consider 5G networks to meet the requirements of the military communications. However, security aspects must be improved to face the challenges of the operational scenarios. Additionally, advanced AI-based security measures are being investigated in forthcoming NextG networks to detect and adapt to emerging threats. In this context, the integration of computer vision techniques in cybersecurity is very promising.In this paper we present a computationally-efficient real-time approach to convert network packets into images directly at base stations. This lightweight implementation aligns well with NextG real-time demands, allowing for the identification of threats at their source. Then we developed a custom Convolutional Neural Network (CNN) operating on the converted packets and aimed at intrusion detection in current and future wireless networks.We then evaluated the performance of this approach to identify and detect malicious content in network traffic utilizing a recent dataset built on a 5G network. Results demonstrate that the designed CNN can achieve high F1-scores, i.e., 0.99593, 0.99860, and 0.99895, across different packet window sizes (10, 50, and 100 packets), indicating that computer vision techniques are promising for detecting malicious network traffic at their source in wireless networks.