In contemporary times, people rely heavily on the internet and search engines to obtain information, either directly or indirectly. However, the information accessible to users constitutes merely 4% of the overall information present on the internet, which is commonly known as the surface web. The remaining information that eludes search engines is called the deep web. The deep web encompasses deliberately hidden information, such as personal email accounts, social media accounts, online banking accounts, and other confidential data. The deep web contains several critical applications, including databases of universities, banks, and civil records, which are off-limits and illegal to access. The dark web is a subset of the deep web that provides an ideal platform for criminals and smugglers to engage in illicit activities, such as drug trafficking, weapon smuggling, selling stolen bank cards, and money laundering. In this article, we propose a search engine that employs deep learning to detect the titles of activities on the dark web. We focus on five categories of activities, including drug trading, weapon trading, selling stolen bank cards, selling fake IDs, and selling illegal currencies. Our aim is to extract relevant images from websites with a ".onion" extension and identify the titles of websites without images by extracting keywords from the text of the pages. Furthermore, we introduce a dataset of images called Darkoob, which we have gathered and used to evaluate our proposed method. Our experimental results demonstrate that the proposed method achieves an accuracy rate of 94% on the test dataset.

翻译：当代社会，人们高度依赖互联网和搜索引擎直接或间接获取信息。然而，用户可访问的信息仅占互联网总信息的4%，这部分通常被称为表层网。搜索引擎无法检索到的其余信息被称为深网。深网包含故意隐藏的信息，例如个人电子邮件账户、社交媒体账户、网上银行账户及其他机密数据。深网具有多种关键应用，包括大学、银行和公民记录等数据库，这些内容受限制且非法访问。暗网是深网的一个子集，为犯罪分子和走私者从事非法活动（如毒品交易、武器走私、出售被盗银行卡及洗钱）提供了理想平台。本文提出了一种采用深度学习检测暗网活动标题的搜索引擎。我们聚焦于五类活动：毒品交易、武器交易、出售被盗银行卡、出售伪造身份证件及出售非法货币。研究目标是从以“.onion”扩展名结尾的网站中提取相关图像，并通过从页面文本中提取关键词来识别无图像的网站标题。此外，我们引入了一个名为Darkoob的图像数据集，该数据集由我们收集并用于评估所提出的方法。实验结果表明，该方法在测试数据集上的准确率达到94%。