Today, all organizations rely heavily on technology, and every piece of technology has security vulnerabilities that can be exploited by malicious actors. Also, as systems become increasingly complex and interconnected, it becomes harder to protect them. On the other hand, these systems provide large and rich data sets on which machine learning thrives and can be used to tackle malicious adversaries. This growing interest in the use of machine learning techniques for cybersecurity is common to both academia and industry.
My research focuses on the application of machine learning methods in various security-related fields, ranging from cyber-physical and automotive systems to binary analysis, going through anomaly and intrusion detection. In particular, I am actively involved in the financial fraud detection domain, where I work on the analysis of advanced financial threats such as banking Trojans and on the design of novel frameworks to identify and timely detect fraudulent transactions.

Michele Carminati is a postdoctoral researcher at Politecnico di Milano. His research interests are routed around the application of machine learning methods for financial fraud detection.

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