Social Media Threat Intelligence: A Framework for Collecting and Categorizing Threat-Related Data

Social Media Threat Intelligence: A Framework for Collecting and Categorizing Threat-Related Data

연구 분야: Safety

학회: International Conference on Digital Forensics and Cyber Crime

The exponential growth of social media, now encompassing approximately five billion users globally, has transformed these platforms into critical sources of information, mirroring diverse societal interactions. However, this vast data repository also introduces significant threats, including terrorism, online fraud, and the spread of disinformation, underscoring the need for robust monitoring and categorization mechanisms. This study presents an innovative framework designed to systematically collect and categorize social media data using weighted keywords tailored to various threat categories. By leveraging semi-automated data collection and keyword weighting techniques, this framework enhances threat detection accuracy and integrates diverse data collection methods such as APIs, bots, and scraping tools. Preliminary results demonstrate the framework’s efficacy in identifying and categorizing threat-related content, highlighting its potential to significantly advance threat intelligence capabilities. This groundbreaking approach promises to revolutionize social media threat intelligence, equipping organizations with the tools to anticipate emerging threats and bolster national security.