Dark Web Content Analysis and Visualization

Abstract

The Dark Web, which is a vast array of encrypted online content and websites that can be only be accessed by the use of anonymizing tools such as The Onion Router (Tor), is currently a topic of serious concern because it has become a growth area for clandestine entities catering to forbidden activities and services such as illegal drugs and weapons, child pornography, sensitive information such as stolen credit card information, distributed denial of services (DDoS) tools such as Booter, and other antisocial activities. As a result, it is now being investigated by researchers, law enforcement agencies, security companies, and academic institutions around the world. However, there is not an efficient investigation method and it takes a lot of time. In an effort to gain a better understanding of the Dark Web, we analyzed a large amount of Onion domain name data obtained from using the "Ichidan" search engine and the "Fresh Onions" open source Tor. More specifically, we collected everything we could gather into an acquired Onion domain and then downloaded the top page. The Onion domain was then classified into six categories, including classification not possible, and a new domain was created from the downloaded top page text. Additionally, we implemented a simulator that is a directed graph created from the uncovered hyperlinks and connection states to the Dark Web were achieved with simplicity. We then attempted to determine the relationships and characteristics of each instance Dark Web content by reflecting on the graphed classification results.