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.

中文翻译：

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使用深度学习对恶意广告统一资源定位符进行分类和聚类

近年来，在学术界和行业中，恶意在线广告检测已引起越来越多的关注。现有的广告拦截系统很容易受到新攻击的影响，并可能通过分析Web内容或查询远程服务器而导致时间延迟问题。本文提出了一种轻量级的广告统一资源定位符（URL）检测系统，该系统仅取决于基于词法的功能。深度学习算法用于在线广告分类。优化深度神经网络架构后，我们提出的方法可以实现令人满意的结果，误报率低至1.31％。我们还设计了一种新颖的无监督数据聚类方法。