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PhD: Mining Internet Payment Development Logs for Reliability & Security

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Job description

Within the recently granted NWO MIPL project, you will work closely together with Dr. Sicco Verwer and Prof. Arie van Deursen to develop novel algorithms for learning behavioural models (state machines) in real-time from vast volumes of software log-lines and methods for analysing these models. You will improve the security of a distributed payment system by inferring probabilistic state machine models from system interactions and log data. Building on technology for inferring complex communication protocols such as SIP, TLS, and EMV, you will develop new algorithms to infer behavioural models from massive logs of Adyen's payment system (https://www.adyen.com). Half of your time will be spent at Adyen in Amsterdam, working closely together with their developers and analysts in order to find anomalies and potential security issues. Your research will be used by software engineering experts to radically innovate the way log-lines are written and used. Together, you will think of new possibilities and evaluate their impact on log-line usability. You will join a team of experts in state machine learning technology, with applications ranging from reversing communication protocols to detecting malware.
Requirements

We invite applicants who meet the following requirements:
• You have an MSc degree in computer science, artificial intelligence, or mathematics.
• You have excellent programming skills and a keen interest in cyber security or machine learning, preferably both.
• You have a good understanding of spoken and written English.
• Published work in software engineering, intrusion detection, reverse-engineering, machine learning, or data (stream) mining is a plus.
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