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Classifying and clustering malicious advertisement uniform resource locators using deep learning

机译:使用深度学习对恶意广告统一资源定位器进行分类和聚类

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摘要

Malicious online advertisement detection has attracted increasing attention in recent years in both academia and industry. The existing advertising blocking systems are vulnerable to the evolution of new attacks and can cause time latency issues by analyzing web content or querying remote servers. This article proposes a lightweight detection system for advertisement Uniform resource locators (URLs) detection, depending only on lexical-based features. Deep learning algorithms are used for online advertising classification. After optimizing the deep neural network architecture, our proposed approach can achieve satisfactory results with false negative rate as low as 1.31%. We also design a novel unsupervised method for data clustering. With the implementation of AutoEncoder for feature preprocessing and t-distributed stochastic neighbor embedding for clustering and visualization, our model outperforms other dimensionality reduction algorithms by generating clear clusterings for different URL families.
机译:近年来,恶意在线广告检测在学术界和工业方面引起了不断的关注。现有的广告阻止系统容易受到新攻击的演变,并且可以通过分析Web内容或查询远程服务器来造成时间延迟问题。本文提出了一种用于广告统一资源定位器(URL)检测的轻量级检测系统,其仅取决于基于词汇的特征。深度学习算法用于在线广告分类。在优化深度神经网络架构之后,我们所提出的方法可以获得低至1.31%的假负率的令人满意的结果。我们还为数据聚类设计了一种新颖的无人监督方法。随着自动编码器的执行功能预处理和T-分布的随机邻居嵌入集群和可视化,我们的模型通过生成不同的URL的家庭明确聚类优于其他降维算法。

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