Darknet and Deepnet Mining for Proactive Cybersecurity Threat Intelligence
This provides a proactive cybersecurity service for cyber-defenders by enabling early threat detection, though it is incremental as it applies existing data mining and machine learning techniques to a specific domain.
The paper tackles the problem of gathering cyber threat intelligence from darknet and deepnet sources, such as hacker forums and marketplaces, to identify emerging threats like new malware and exploits before deployment, resulting in an average of 305 high-quality threat warnings per week with machine learning models achieving 92% recall for products and 80% for discussions.
In this paper, we present an operational system for cyber threat intelligence gathering from various social platforms on the Internet particularly sites on the darknet and deepnet. We focus our attention to collecting information from hacker forum discussions and marketplaces offering products and services focusing on malicious hacking. We have developed an operational system for obtaining information from these sites for the purposes of identifying emerging cyber threats. Currently, this system collects on average 305 high-quality cyber threat warnings each week. These threat warnings include information on newly developed malware and exploits that have not yet been deployed in a cyber-attack. This provides a significant service to cyber-defenders. The system is significantly augmented through the use of various data mining and machine learning techniques. With the use of machine learning models, we are able to recall 92% of products in marketplaces and 80% of discussions on forums relating to malicious hacking with high precision. We perform preliminary analysis on the data collected, demonstrating its application to aid a security expert for better threat analysis.