论文标题
Eagernet：对计算有效入侵检测的神经网络的早期预测
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion Detection
论文作者
论文摘要
近年来，完全连接的神经网络（FCNN）一直是大多数最先进的机器学习（ML）应用程序的核心，并且已被广泛用于入侵检测系统（IDSS）。上几年的实验结果表明，通常具有更多层的更深层神经网络的性能要比浅层模型更好。尽管如此，尽管使用特殊硬件（例如GPU），但随着层次越来越多的层次，以更少的资源进行快速预测已成为一项艰巨的任务。我们提出了一个新的体系结构，以最少的资源来检测网络攻击。该体系结构能够处理二进制或多类分类问题，并为网络的准确性提供预测速度。我们使用两个不同的网络入侵检测数据集评估我们的建议。结果表明，可以在不评估大多数样品的所有层的情况下获得与简单FCNN的可比精度，从而获得早期预测并节省能量和计算工作。
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.
