| Title | Threats and data trading detection methods in the dark web |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Li, Junyan |
| Conference Name | 2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA) |
| Date Published | nov |
| Keywords | Buildings, cryptography, cyber-attacks, cyber-security, dark web, Data security, Databases, drugs, Human Behavior, Intelligent systems, process control, pubcrawl, visualization |
| Abstract | The dark web has become a major trading platform for cybercriminals, with its anonymity and encrypted content nature make it possible to exchange hacked information and sell illegal goods without being traced. The types of items traded on the dark web have increased with the number of users and demands. In recent years, in addition to the main items sold in the past, including drugs, firearms and child pornography, a growing number of cybercriminals are targeting various types of private information, including different types of account data, identity information and visual data etc. This paper will further discuss the issue of threat detection in the dark web by reviewing the past literature on the subject. An approach is also proposed to identify criminals who commit crimes offline or on the surface network by using private information purchased from the dark web and the original sources of information on the dark web by building a database based on historical victim records for keyword matching and traffic analysis. |
| DOI | 10.1109/CITISIA53721.2021.9719947 |
| Citation Key | li_threats_2021 |
Threats and data trading detection methods in the dark web