针对边缘设备的网络安全检测，提出自适应剪枝方法提升模型效率。
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge
- 根据资源约束动态调整神经网络剪枝程度。
- 在新数据集上仅少数剪枝算法表现稳定且有效。
- 适合资源受限的嵌入式入侵检测系统部署。
神经网络剪枝通过减少模型规模来提升推理速度或降低内存占用，同时尽量保持预测性能。本文研究多种剪枝方法在新型网络安全数据集上的泛化能力，采用比原设计更简单的网络结构进行测试，并评估不同剪枝率下的表现。实验表明，多数剪枝方法在新场景下表现不佳，仅有少数算法能保持可接受的性能，验证了自适应剪枝对边缘环境的必要性。
原文摘要 · Abstract (English)
Artificial neural network pruning is a method in which artificial neural network sizes can be reduced while attempting to preserve the predicting capabilities of the network. This is done to make the model smaller or faster during inference time. In this work we analyze the ability of a selection of artificial neural network pruning methods to generalize to a new cybersecurity dataset utilizing a simpler network type than was designed for. We analyze each method using a variety of pruning degrees to best understand how each algorithm responds to the new environment. This has allowed us to determine the most well fit pruning method of those we searched for the task. Unexpectedly, we have found that many of them do not generalize to the problem well, leaving only a few algorithms working to an acceptable degree.
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