EagerNet：用于计算高效入侵检测的神经网络早期预测
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion Detection
摘要：
全连接神经网络（FCNN）是近年来最先进的机器学习（ML）应用的核心，也被广泛应用于入侵检测系统（IDS）。过去几年的实验结果表明，通常具有更多层的更深神经网络比浅模型表现更好。尽管如此，随着层数的增加，尽管使用了GPU等特殊硬件，但用更少的资源获得快速预测已成为一项艰巨的任务。我们提出了一种新的架构，以检测网络攻击，以最少的资源。该架构能够处理二进制或多类分类问题，并以预测速度换取网络的准确性。我们用两个不同的网络入侵检测数据集来评估我们的建议。结果表明，在不对大多数样本的所有层进行评估的情况下，可以获得与简单FCNN相当的精度，从而获得早期预测并节省能源和计算工作量。
英文摘要：
Fully Connected Neural Networks (FCNNs) have been the core of most state-of-the-art Machine Learning (ML) applications in recent years and also have been widely used for Intrusion Detection Systems (IDSs). Experimental results from the last years show that generally deeper neural networks with more layers perform better than shallow models. Nonetheless, with the growing number of layers, obtaining fast predictions with less resources has become a difficult task despite the use of special hardware such as GPUs. We propose a new architecture to detect network attacks with minimal resources. The architecture is able to deal with either binary or multiclass classification problems and trades prediction speed for the accuracy of the network. We evaluate our proposal with two different network intrusion detection datasets. Results suggest that it is possible to obtain comparable accuracies to simple FCNNs without evaluating all layers for the majority of samples, thus obtaining early predictions and saving energy and computational efforts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
