Harnessing TI Feeds for Exploitation Detection

Many organizations rely on Threat Intelligence (TI) feeds to assess the risk associated with security threats. Due to the volume and heterogeneity of data, it is prohibitive to manually analyze the threat information available in different loosely structured TI feeds. Thus, there is a need to develop automated methods to vet and extract actionable information from TI feeds. To this end, we present a machine learning pipeline to automatically detect vulnerability exploitation from TI feeds. We first model threat vocabulary in loosely structured TI feeds using state-of-the-art embedding techniques (Doc2Vec and BERT) and then use it to train a supervised machine learning classifier to detect exploitation of security vulnerabilities. We use our approach to identify exploitation events in 191 different TI feeds. Our longitudinal evaluation shows that it is able to accurately identify exploitation events from TI feeds only using past data for training and even on TI feeds withheld from training. Our proposed approach is useful for a variety of downstream tasks such as data-driven vulnerability risk assessment.

Cream Skimming the Underground: Identifying Relevant Information Points from Online Forums

This paper proposes a machine learning-based approach for detecting the exploitation of vulnerabilities in the wild by monitoring underground hacking forums. The increasing volume of posts discussing exploitation in the wild calls for an automatic approach to process threads and posts that will eventually trigger alarms depending on their content. To illustrate the proposed system, we use the CrimeBB dataset, which contains data scraped from multiple underground forums, and develop a supervised machine learning model that can filter threads citing CVEs and label them as Proof-of-Concept, Weaponization, or Exploitation. Leveraging random forests, we indicate that accuracy, precision and recall above 0.99 are attainable for the classification task. Additionally, we provide insights into the difference in nature between weaponization and exploitation, e.g., interpreting the output of a decision tree, and analyze the profits and other aspects related to the hacking communities. Overall, our work sheds insight into the exploitation of vulnerabilities in the wild and can be used to provide additional ground truth to models such as EPSS and Expected Exploitability.