Human-as-a-security-sensor for harvesting threat intelligence

Humans are commonly seen as the weakest link in corporate information security. This led to a lot of effort being put into security training and awareness campaigns, which resulted in employees being less likely the target of successful attacks. Existing approaches, however, do not tap the full potential that can be gained through these campaigns. On the one hand, human perception offers an additional source of contextual information for detected incidents, on the other hand it serves as information source for incidents that may not be detectable by automated procedures. These approaches only allow a text-based reporting of basic incident information. A structured recording of human delivered information that also provides compatibility with existing SIEM systems is still missing. In this work, we propose an approach, which allows humans to systematically report perceived anomalies or incidents in a structured way. Our approach furthermore supports the integration of such reports into analytics systems. Thereby, we identify connecting points to SIEM systems, develop a taxonomy for structuring elements reportable by humans acting as a security sensor and develop a structured data format to record data delivered by humans. A prototypical human-as-a-security-sensor wizard applied to a real-world use-case shows our proof of concept.