Title: Enhanced Crime and Threat Intelligence Hunter with Named Entity Recognition and Sentiment Analysis

Issue Number: Vol. 11, No. 2
Year of Publication: 2022
Page Numbers: 20-33
Authors: James H. Ng, Peter K.K. Loh
Journal Name: International Journal of Cyber-Security and Digital Forensics (IJCSDF)
- Hong Kong


Collecting cybercrime evidence on the Internet typi-cally involves reconnaissance and analyses of infor-mation extracted. Scouring the Internet, especially the Deep Web, often requires manual effort and is time-consuming. Hence, it is imperative to have an efficient framework for an intelligent tool to gather and correlate cyber intel automatically according to programmed directives. In this paper, we present an updated design to our prior threat intelligence hunter [1]. We make use of machine learning and sentiment analysis for parsing and correlating linguistic intel.