The objective of this lab is to analyze several data sets containing information relevant to quantify cyber risks, associated
with human behaviors or with frequency and severity of cyber security events. Data and some methods will be prepared in advance
to allow going deep as quick as possible.
Students can work alone or in groups with the objective to uncover and characterize a surprising finding to be shared with the whole group at the end of the lab. Characterization can be achieved through relevant statistics, some machine learning, a nice visualization or any other relevant means. Pizza and drinks will be served at the end of the day.
Structure: Interactive case studies, exercises with laptops
Required Skills: Statistics, data mining/science, in particular Python (numpy, scipy, matplotlib, pandas, scikit-Learn), data visualization is also welcome (e.g., d3.js)
Required Equipment: Laptop is required and Python with scientific suite (e.g. Anaconda) or any other means relevant for the achievement the objectives of the lab
Maximal number of participants: 30
Thomas Maillart pursues academic research on the mechanisms of cyber risks and collective intelligence with focus on the
underlying economic incentives and associated human behaviors.
Thomas has co-founded a cybersecurity startup in 2005 and has consulted on cyber risks for various governmental and private organizations (Swiss Confederation, US DHS, medical and banking industries).