“Generating Threat Intelligence based on OSINT and a Cyber Threat Unified Taxonomy”
Master’s thesis, Mestrado em Segurança Informática, Departamento de Informática, Faculdade de Ciências da Universidade de Lisboa, Sept. 2020
Abstract: Today’s threats use multiple means of propagation, such as social engineering, email, and application vulnerabilities, and often operate in different phases, such as single device compromise, network lateral movement and data exfiltration. These complex threats rely on well-advanced tactics for appearing unknown to traditional security defences. One type that had a major impact in the rise of cybercrime are the advanced persistent threats (APTs), which have clear objectives, are highly organized and wellresourced and tend to perform long term stealthy campaigns with repeated attempts. As organizations realize that attacks are increasing in size and complexity, threat intelligence (TI) is growing in popularity and use amongst them. This trend followed the evolution of the APTs as they require a different level of response that is more specific to the organization. TI can be obtained via many formats, being open source intelligence (OSINT) one of the most common; and using threat intelligence platforms (TIPs)that aid organization consuming, producing and sharing TI. TIPs have multiple advantages that enable organisations to easily bootstrap the core processes of collecting, normalising, enriching, correlating, analysing, disseminating and sharing of threat related information. However, current TIPs have some limitations that prevents theirs mass adoption. This dissertation proposes a solution to some of these limitations related with threat knowledge management, limited technology enablement in threat triage, high volume of shared threat information, data quality and limited advanced analytics capabilities and tasks automation. Overall, our solution improves the quality of TI by classifying it accordingly a common taxonomy, removing the information with low value, enriching it with valuable information from OSINT sources, and aggregating it into clusters of events with similar information. This dissertation offers a complete data analysis of three OSINT feeds and the results that made us to design our solution, a detailed description of the architecture of our solution, its implementations and its validation, including the processing of events from other academic solutions.
Research line(s): Fault and Intrusion Tolerance in Open Distributed Systems (FIT)