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.

翻译：入侵检测在广阔且持续互联的环境中存在重大挑战。随着恶意代码不断演进及复杂攻击方法日益增多，各类基于深度学习的先进检测方案已被提出。然而，入侵检测模型的复杂性与准确性仍需进一步提升，以使其更适用于不同系统类别，特别是在资源受限设备（如边缘计算系统嵌入设备）中。本研究提出一种三阶段训练范式，并辅以增强型剪枝方法与模型压缩技术，旨在提升系统效能，同时维持高精度入侵检测水平。基于UNSW-NB15数据集的实证评估表明，该方案在保持与同类方案相当准确率的同时，显著降低了模型规模。