Visible to the public An Expert System for Classifying Harmful Content on the Dark Web

TitleAn Expert System for Classifying Harmful Content on the Dark Web
Publication TypeConference Paper
Year of Publication2020
AuthorsKobayashi, H., Kadoguchi, M., Hayashi, S., Otsuka, A., Hashimoto, M.
Conference Name2020 IEEE International Conference on Intelligence and Security Informatics (ISI)
Date PublishedNov. 2020
PublisherIEEE
ISBN Number978-1-7281-8800-3
Keywordsdark web, expert system, explainable AI, Human Behavior, human factors, pubcrawl
Abstract

In this research, we examine and develop an expert system with a mechanism to automate crime category classification and threat level assessment, using the information collected by crawling the dark web. We have constructed a bag of words from 250 posts on the dark web and developed an expert system which takes the frequency of terms as an input and classifies sample posts into 6 criminal category dealing with drugs, stolen credit card, passwords, counterfeit products, child porn and others, and 3 threat levels (high, middle, low). Contrary to prior expectations, our simple and explainable expert system can perform competitively with other existing systems. For short, our experimental result with 1500 posts on the dark web shows 76.4% of recall rate for 6 criminal category classification and 83% of recall rate for 3 threat level discrimination for 100 random-sampled posts.

URLhttps://ieeexplore.ieee.org/document/9280536
DOI10.1109/ISI49825.2020.9280536
Citation Keykobayashi_expert_2020