A Deep Learning Approach to Create DNS Amplification Attacks
Jared Mathews Shankar Banik Cory Nance Prosenjit Chatterjee
作者信息
Abstract
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.引用本文复制引用
Jared Mathews,Shankar Banik,Cory Nance,Prosenjit Chatterjee.A Deep Learning Approach to Create DNS Amplification Attacks[EB/OL].(2022-06-28)[2026-04-04].https://arxiv.org/abs/2206.14346.学科分类
安全科学/计算技术、计算机技术/电子技术应用
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