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A comprehensive cybersecurity evaluation of automotive on-board networks has become a crucial antecedent to the commercial distribution of vehicles. However, the means to perform the required testing are limited due to the black-box nature and complexity of automotive systems. To rectify this, several approaches have been put forward to systematise and automate the process of testing vehicular systems, but these still require a significant amount of expert input to build test cases. Accordingly, this work aims to further automate the process by introducing a machine learning based generation scheme for data which can then be used to facilitate the creation of further tests.