Two-Stage Classification Technique for Malicious DNS Identification

G. Amaizu, Danielle Jaye S. Agron, Jae-Min Lee, Dong-Seong Kim
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Abstract

Cyber-security for years has been a challenging topic for the research community and most of these attacks have been directed at one of the most critical Internet infrastructure, the domain name system (DNS). DNS attacks are usually catastrophic and often results in loss of sensitive information, hence this paper aims at proffering a solution to these type of attacks. In this paper, a two-stage classification process is proposed for mitigating DNS attacks. The proposed scheme employs long short-term memory in the first stage a convolutional neural network at the second stage. Simulation results show a good classification accuracy for both stages of the proposed scheme.
恶意DNS识别的两阶段分类技术
多年来,网络安全一直是研究界的一个具有挑战性的话题,大多数攻击都是针对最关键的互联网基础设施之一,域名系统(DNS)。DNS攻击通常是灾难性的,并且经常导致敏感信息的丢失,因此本文旨在为这类攻击提供解决方案。本文提出了一种两阶段分类方法来减轻DNS攻击。该方案第一阶段采用长短期记忆,第二阶段采用卷积神经网络。仿真结果表明,该方法在两个阶段均具有较好的分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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