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As the communication and control of cars, buses and trucks is getting smarter, so are the threats from malicious attempts to compromize security of these vehicles. The problem of detecting and possibly classifying such attempts is approached from the classical statistical change-point detection angle but also from the newer machine learning side. Also hybrid methods could be considered depending on how the project proceeds. The goal is to develop new cloud methods and to evaluate the methods in comparison to state-of-the-art methods used today. Possibly, new performance measures could be constructed in order to better understand how well the suggested methods behave for the environment-specific problems. The methods could possibly be demonstrated in examples with real data. The thesis will be a part of the CyRev research project in collaboration with partners from the industry.
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