Labeling Hacker Exploits for Proactive Cyber Threat Intelligence: A Deep Transfer Learning Approach
Benjamin Ampel, Sagar Samtani, Hongyi Zhu, Steven Ullman, Hsinchun Chen
Abstract
With the rapid development of new technologies, vulnerabilities are at an all-time high. Companies are investing in developing Cyber Threat Intelligence (CTI) to counteract these new vulnerabilities. However, this CTI is generally reactive based on internal data. Hacker forums can provide proactive CTI value through automated analysis of new trends and exploits. One way to identify exploits is by analyzing the source code that is posted on these forums. These source code snippets are often noisy and unlabeled, making standard data labeling techniques ineffective. This study aims to design a novel framework for the automated collection and categorization of hacker forum exploit source code. We propose a deep transfer learning framework, the Deep Transfer Learning for Exploit Labeling (DTL-EL). DTL-EL leverages the learned representation from professional labeled exploits to better generalize to hacker forum exploits. This model classifies the collected hacker forum exploits into eight predefined categories for proactive and timely CTI. The results of this study indicate that DTL-EL outperforms other prominent models in hacker forum literature.