Are We Shooting Flies with Cannons? Trade-off Analysis for AI-based 5G Intrusion Detection

2026-08-27

AI Summary

本文探讨了5G网络入侵检测中AI模型的性能与计算成本之间的权衡，发现传统机器学习模型如XGBoost相比深度神经网络和大型语言模型，在保持高性能的同时显著降低了计算成本。

Abstract

The increasing adoption of Artificial Intelligence (AI) in network intrusion detection raises the question of whether complex and computationally expensive models are justified for this task. In this work, we investigate the trade-off between detection performance and computational cost for intrusion detection in 5G network telemetry. We compare traditional machine learning (ML) models, including XGBoost as a representative of tree ensemble, and TabNet for tabular deep neural network (DNN), with a large language model (LLM) used as a general-purpose intrusion detector. The LLM is evaluated under both zero-shot and few-shot prompting configurations. We evaluate the models in terms of detection performance, inference time, and CPU time as a proxy for energy efficiency. Using a relatively large available 5G dataset, we show that traditional ML models consistently achieve near-perfect detection performance with negligible inference time, while LLM-based approaches perform significantly worse and incur orders-of-magnitude higher CPU usage. Few-shot prompting improves recall, but at the cost of lower accuracy and further increased CPU time, without closing the performance gap. These findings indicate that, for tabular intrusion detection in 5G networks, XGBoost offers a substantially better performance-cost trade-off than DNNs and LLMs, highlighting the importance of selecting models based on task suitability rather than increasing complexity.

Problem

• AI-based 5G Intrusion Detection
• Trade-off Analysis
• Computational Cost
• Detection Performance
• Model Complexity

Innovation

• trade-off analysis
• computational cost
• detection performance
• XGBoost
• large language model