Intrusion detection poses a significant challenge within expansive and
persistently interconnected environments. As malicious code continues to
advance and sophisticated attack methodologies proliferate, various advanced
deep learning-based detection approaches have been proposed. Nevertheless, the
complexity and accuracy of intrusion detection models still need further
enhancement to render them more adaptable to diverse system categories,
particularly within resource-constrained devices, such as those embedded in
edge computing systems. This research introduces a three-stage training
paradigm, augmented by an enhanced pruning methodology and model compression
techniques. The objective is to elevate the system's effectiveness,
concurrently maintaining a high level of accuracy for intrusion detection.
Empirical assessments conducted on the UNSW-NB15 dataset evince that this
solution notably reduces the model's dimensions, while upholding accuracy
levels equivalent to similar proposals.

该研究引入了一种三阶段训练模型，结合增强的剪枝方法和模型压缩技术，以提高入侵检测系统的效果，并同时保持高准确性水平。实证评估结果表明，该解决方案显著减小了模型的维度，同时保持了与类似提议相当的准确性水平。

边缘计算设备中用于入侵检测的有效多阶段训练模型

Effective Multi-Stage Training Model For Edge Computing Devices In  Intrusion Detection

Large Language Models (LLM) and foundation models are popular as they offer
new opportunities for individuals and businesses to improve natural language
processing, interact with data, and retrieve information faster. However,
training or fine-tuning LLMs requires a vast amount of data, which can be
challenging to access due to legal or technical restrictions and may require
private computing resources. Federated Learning (FL) is a solution designed to
overcome these challenges and expand data access for deep learning
applications.
This paper takes a hardware-centric approach to explore how LLMs can be
brought to modern edge computing systems. Our study fine-tunes the FLAN-T5
model family, ranging from 80M to 3B parameters, using FL for a text
summarization task. We provide a micro-level hardware benchmark, compare the
model FLOP utilization to a state-of-the-art data center GPU, and study the
network utilization in realistic conditions. Our contribution is twofold:
First, we evaluate the current capabilities of edge computing systems and their
potential for LLM FL workloads. Second, by comparing these systems with a
data-center GPU, we demonstrate the potential for improvement and the next
steps toward achieving greater computational efficiency at the edge.

通过硬件为中心的方法，本研究探讨了如何将大型语言模型 (LLMs) 应用于现代边缘计算系统，并使用联邦学习 (FL) 对 FLAN-T5 模型家族进行微调，以进行文本摘要任务。通过与数据中心 GPU 的比较，我们评估了边缘计算系统的当前能力以及它们在 LLM FL 工作负载方面的潜力，并展示了在边缘端实现更大计算效率的潜力与下一步的发展方向。