Abstract
Forecasting cyberattacks before they occur is an important yet challenging task, as exploring early signs of an attack from a large volume of data is not trivial. This paper describes the design and evaluation of a novel automated system, CAPTURE, which uses a broad range of unconventional signals derived from various open sources to forecast cyberattacks towards a target organization anonymized as CorpX. It includes novel approaches to select relevant and significant, but not redundant, lagged signals and treat the non-stationary relationships between the unconventional signals and the cyberattack occurrences. Using cyber incidents recorded by a third party organization and 146 signals from a variety of sources, this paper demonstrates that CAPTURE performs significantly better than a baseline model with various configurations. Furthermore, CAPTURE offers insights to human analysts on which and how specific lagged signals contributed to the forecasts.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
