Abstract
The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA) in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.
Highlights
The Dark Net, referred to as the Dark Web, gains more attention from individuals who are concerned about their online privacy, since it is focused on providing user anonymity [1]
The study conducted by [3] shows that the most common concern for the people involved in technological platforms is widespread data collection
We used the improved support vector machine-enabled radial basis function classifier to analyze the data for topics and state of legality and non-legality [29]
Summary
The Dark Net, referred to as the Dark Web, gains more attention from individuals who are concerned about their online privacy, since it is focused on providing user anonymity [1]. Connection to the network is performed by using special browsers They are focused on onion routing use. The majority of users show legitimate behavior, as the study of [5] states that most of the Dark Web’s users may have never visited websites ending with “.onion” and have used it instead for secure browsing. This is proven by the low percentage of network traffic, corresponding to the range of 6–7% [5] leading to those sites
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