论文标题
对基于深度学习的IDS中不同类型的神经网络的对抗培训的评估
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs
论文作者
论文摘要
网络安全应用程序，包括深神经网络的入侵检测系统，正在迅速增加，以使异常活动的检测任务更加准确和强大。随着使用DNN的迅速增加以及通过系统传播的数据量，不同种植类型的对抗性攻击以击败它们带来了严重的挑战。在本文中，我们专注于研究不同逃避攻击的有效性，以及如何使用不同的神经网络（例如卷积神经网络（CNN））和复发性神经网络（RNN）训练弹性深度学习的ID。我们使用Min-Max方法来使用两个基准数据集来制定针对对抗示例的训练鲁棒ID的问题。我们对不同深度学习算法和不同基准数据集的实验表明，使用基于对抗性训练的Min-Max方法的防御可提高针对五种众所周知的对抗性攻击方法的鲁棒性。
Network security applications, including intrusion detection systems of deep neural networks, are increasing rapidly to make detection task of anomaly activities more accurate and robust. With the rapid increase of using DNN and the volume of data traveling through systems, different growing types of adversarial attacks to defeat them create a severe challenge. In this paper, we focus on investigating the effectiveness of different evasion attacks and how to train a resilience deep learning-based IDS using different Neural networks, e.g., convolutional neural networks (CNN) and recurrent neural networks (RNN). We use the min-max approach to formulate the problem of training robust IDS against adversarial examples using two benchmark datasets. Our experiments on different deep learning algorithms and different benchmark datasets demonstrate that defense using an adversarial training-based min-max approach improves the robustness against the five well-known adversarial attack methods.
