PhD position Learning State Machines from Software Logs

Within dr. Sicco Verwer's VIDI project "Learning state machines from infrequent software traces", you will develop novel model (automaton) learning algorithms and develop tools for learning from software log data. The aim is to provide software analysts with insightful models. We are able to learn insightful state machine models for the frequent happy flow of software. Although these can be useful to understand software, the real interesting behavior occurs in the infrequent unhappy flows, typically caused by errors. The project aims at learning state machines from such unhappy flows. Using techniques from outlier explanation we aim to uncover how unhappy flows differ from happy ones. We will work close together with developers and analysts from our industrial partner Adyen to develop a tool that helps them to understand software/network logs and errors.

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) is known worldwide for its high academic quality and the social relevance of its research programmes. The faculty’s excellent facilities accentuate its international position in teaching and research. Within this interdisciplinary and international setting the faculty employs more than 1100 employees, including about 400 graduate students and about 2100 students. Together they work on a broad range of technical innovations in the fields software technology and intelligent systems.

Within the cyber analytics lab (www.cyber-analytics.nl ) in the cyber security group (www.tudelft.nl/ewi/over-de-faculteit/afdelingen/intelligent-systems/cybe... ), we work on understanding IT systems. Almost every IT system that we use is complex and contains many mistakes. Every single mistake is a wide-open door to attackers trying to break into the system. Humans cannot deal with this complexity, so we are working on automated tools that can.