CAREER: An Artificial Intelligence (AI)-enabled Analytics Perspective for Developing Proactive Cyber Threat Intelligence

Indiana University, Bloomington IN

Investigators

Abstract

Cyber-attacks continue to exact a terrible toll on modern society. Increasingly, many firms seek to integrate cyber threat intelligence (CTI) about emerging threats and their relevance to vulnerabilities within their assets. However, much of the current CTI analyst practice is reactive, in which analysis manually examines exploits (software that circumvents vulnerabilities and allows an attacker to manipulate cyber-assets) after an attack. More proactive approaches to using CTI might prevent many cyber-attacks; in particular, sources such as Dark Web hacker forums often include signals about possible emerging exploit trends and attack vectors. However, these large, international, and ever-evolving platforms often contain millions of posts, a scale that makes conventional CTI analysis prohibitive, limited, error-prone, and time-consuming. Therefore, this project seeks to develop Artificial Intelligence (AI)-enabled analytics techniques based on text analysis and network science to identify emerging trends and to link exploits to vulnerabilities. The models and results produced from this research will be integrated into curricula for cybersecurity students and data scientists to help rapidly grow a well-trained workforce in proactive AI-enabled CTI analytics. This CAREER project seeks to develop two thrusts of proactive CTI research. The first is a novel Diachronic Graph Transformer (DGT) to detect and predict emerging exploit terms, semantics, and trends through advancing methods for balancing word embedding stability and capturing their shifts over time. The second is a self-supervised neural information retrieval method, entitled the Exploit-Vulnerability Self-Supervised Linker, that links hacker exploits to vulnerabilities in a manner consistent with CTI analysts' procedures and that accounts for a technology's configurations, dependencies, and other characteristics. The data and methods from this research will be integrated into three thrusts to improve cyber-AI education: (1) new lessons for an AI for Cybersecurity course at Indiana University, (2) resources to enhance the cybersecurity curricula of NSF CyberCorps Scholarship-for-Service institutions, and (3) proactive CTI learning modules for the AI+ platform offered by the Open Data Science Conference, one of the world's largest data science communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.