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.Comment: 11 pages , 16 figures , 2 tables , New Dataset For DarkWeb Activity
Classificatio