Probabilistic Methods for Secure Learning and Control

Prob4Sec aims to develop theory and probabilistic methods for securing cyber-physical systems (CPSs) possessing learning and control capabilities: digital devices that learn from data they collect, and automatically decide how to best interact with the physical world over time.Intelligent CPSs are pervasive in our society.

Users rely and trust that these devices will operate in a safe and secure manner. The consequences of incorrect behaviors in CPSs can be dire, threatening the users´ well-being.

Securing these systems is of utmost importance.Probabilistic methods are a natural way to deal with the uncertainty of physical systems and unintentional failures.

Mathematical and statistical tools enable us to take a small number of samples and still be able to draw general conclusions.

However, attacks are not only probabilistic but can have strategic behaviors, tailored to causing harm while avoiding detection.

Hence, it is critical to develop systematic approaches that integrate the probabilistic and the strategic aspects of attacks in security analysis.Prob4Sec combines knowledge from security, statistical methods, and AI and control engineering to develop new methods for designing secure CPSs with learning and control functionalities.

These methods will be instrumental in preventing future threats to all who benefit from this technology.

Prob4Sec will provide further tools based on which trust in new technologies can be built and communicated, from developers to end-users.