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Conference Paper
Furumoto, Keisuke, Umizaki, Mitsuhiro, Fujita, Akira, Nagata, Takahiko, Takahashi, Takeshi, Inoue, Daisuke.  2021.  Extracting Threat Intelligence Related IoT Botnet From Latest Dark Web Data Collection. 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing Communications (GreenCom) and IEEE Cyber, Physical Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). :138—145.
As it is easy to ensure the confidentiality of users on the Dark Web, malware and exploit kits are sold on the market, and attack methods are discussed in forums. Some services provide IoT Botnet to perform distributed denial-of-service (DDoS as a Service: DaaS), and it is speculated that the purchase of these services is made on the Dark Web. By crawling such information and storing it in a database, threat intelligence can be obtained that cannot otherwise be obtained from information on the Surface Web. However, crawling sites on the Dark Web present technical challenges. For this paper, we implemented a crawler that can solve these challenges. We also collected information on markets and forums on the Dark Web by operating the implemented crawler. Results confirmed that the dataset collected by crawling contains threat intelligence that is useful for analyzing cyber attacks, particularly those related to IoT Botnet and DaaS. Moreover, by uncovering the relationship with security reports, we demonstrated that the use of data collected from the Dark Web can provide more extensive threat intelligence than using information collected only on the Surface Web.