论文标题
一种深度学习的方法来创建DNS扩增攻击
A Deep Learning Approach to Create DNS Amplification Attacks
论文作者
论文摘要
近年来，深度学习表明自己是网络安全方面非常有价值的工具，因为它有助于网络入侵检测系统对攻击进行分类和检测。对抗性学习是利用机器学习来生成一组扰动的输入集以馈送到神经网络以错误分类的过程。目前在对抗性学习领域的许多工作都是在图像处理和自然语言处理中使用各种算法进行的。感兴趣的两种算法是对深神经网络和TextAttack的弹性网络攻击。在我们的实验中，EAD和TextAttack算法应用于域名系统放大分类器。该算法用于生成恶意分布式拒绝服务对抗示例，然后作为网络入侵检测系统神经网络的输入以将其作为输入，以将其分类为有效的流量。我们在这项工作中表明，图像处理和自然语言处理对抗性学习算法都可以针对网络入侵检测神经网络。
In recent years, deep learning has shown itself to be an incredibly valuable tool in cybersecurity as it helps network intrusion detection systems to classify attacks and detect new ones. Adversarial learning is the process of utilizing machine learning to generate a perturbed set of inputs to then feed to the neural network to misclassify it. Much of the current work in the field of adversarial learning has been conducted in image processing and natural language processing with a wide variety of algorithms. Two algorithms of interest are the Elastic-Net Attack on Deep Neural Networks and TextAttack. In our experiment the EAD and TextAttack algorithms are applied to a Domain Name System amplification classifier. The algorithms are used to generate malicious Distributed Denial of Service adversarial examples to then feed as inputs to the network intrusion detection systems neural network to classify as valid traffic. We show in this work that both image processing and natural language processing adversarial learning algorithms can be applied against a network intrusion detection neural network.
