MALAGA

The rise of connectivity and automation technology is creating more and more opportunities in many fields. Connected and autonomous vehicles (CAV) is a field widely affected by these technology advancements. While innovation is disrupting business standards and encouraging investments, it is also at risk of cybersecurity attacks.

The EU-funded MALAGA project will use machine learning (ML) technology to investigate cybersecurity risks, as well as find ways to reduce risks in the CAV field. With ML, the project will predict risks and price insurance policies to pave the way for innovation and entrepreneurial activity in Europe.

The research will examine Connected and Autonomous Vehicles (CAV) cybersecurity risks and mitigation using Machine Learning (ML) techniques to predict future risks, price insurance policies and and thereby foster innovation and entrepreneurial activity in Europe. My research will go beyond the SoA and implement models in ML like ensemble models and deep learning to forecast the risks of CAV technology. A network model of interactions will be trained and evaluated to study cascading of risks and threats in the CAV environment.

The host team at the University of Limerick have members with machine learning skills, actuarial skills, ethical skills and underwriting experience. I will have access to staff development programmes, training courses, workshops, online courses and internal meetings. My host team are directly connected to a large variety of colleagues in other EU locations in both academic and industry positions. I will work with my host and partners to develop my research and increase my skillsets.

The research directly contributes to several UN sustainable development goals. On a personal level, the impact of my fellowship and collaborations will expand my set of skills, both research-related and transferable ones, leading to greatly improved career prospects both in and outside academia. My new abilities will include enhanced machine learning capabilities, cyber risk expertise and risk engineering skills.