Barker, Ken E.Devereux, Hannah2019-06-172019-06-172019-06-17Devereux, H. (2019). Data-Driven Cyber Prediction in Hybrid Warfare (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/110505Cyber Warfare, despite being a thoroughly discussed tactic, is consistently misunderstood and taken out of context. Cyberattacks, most often committed during hybrid warfare, are often studied apart from the physical attributes of war. There is a lack of literature that studies the interplay of cyber and physical attributes within hybrid warfare. By analyzing and assessing the Ukrainian Crisis, this thesis investigates how physical attributes may be used to predict cyberattacks based on real world data. Using the Axelrod-Iliev equation optimal timing of cyberattack against Ukraine could be determined and, from this, defensive postures could be suggested. To test if the Axelrod-Iliev equation held true, statistical analysis was used. The statistical analysis verified the findings of the Axelrod-Iliev equation and provided groundwork for future research in the subject area. The statistical analysis found a lack of correlation between Military Personnel/Policemen Killed/Wounded and cyberattacks, Civilians/Politicians Killed/Wounded and cyberattacks, Protests and cyberattacks. Conversely it strongly suggested links between Bombings and cyberattack, and Open Firing and cyberattacks, which can be expounded upon to further understand the interplay of cyber and physical attributes in hybrid warfare. Keywords: Hybrid warfare, Cyber, Military, Ukraine, Russia, Data AnalyticsengUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Data AnalyticsUkraineRussiaCyberHybrid WarfareMilitary StudiesComputer Sciencemaster thesis10.11575/PRISM/36642