Deep Learning-based Algorithm for Detecting Counterfeit Domain Names

Zhao Wang, Wenhui Yang
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Abstract

To address the two major problems of machine learning: the high cost of manually extracting features and the requirement for reasonable and high relevance of feature set input, this paper will use the ability of deep learning to automatically characterize and learn data to build a lightweight counterfeit domain name deep learning grid-based detection model based only on domain name strings, and conduct comparison experiments with edit distance-based detection models and visual feature-based detection models by testing on public datasets to verify the effectiveness of this deep learning-based counterfeit domain name detection model for counterfeit domain name detection.
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基于深度学习的假冒域名检测算法
为了解决机器学习的两个主要问题:针对人工提取特征的高成本和对特征集输入合理、高相关性的要求,本文将利用深度学习对数据进行自动表征和学习的能力,构建一个仅基于域名字符串的轻量级假冒域名深度学习网格检测模型。并通过在公共数据集上的测试,与基于编辑距离的检测模型和基于视觉特征的检测模型进行对比实验,验证基于深度学习的假冒域名检测模型用于假冒域名检测的有效性。
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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