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Combatting the modification of automotive control systems is a current and future challenge for OEMs and suppliers. ‘Chip-tuning’ is a manifestation of manipulation of a vehicle's original setup and calibration. With the increase in automotive functions implemented in software and corresponding business models, chip tuning will become a major concern. Recognizing and reporting of tuned control units in a vehicle is required for technical as well as legal reasons. This work approaches the problem by capturing the behavior of relevant control units within a machine learning system called a recognition module. The recognition module continuously monitors vehicle's sensor data. It comprises a set of classifiers that have been trained on the intended behavior of a control unit before the vehicle is delivered. When the vehicle is on the road, the recognition module uses the classifier together with current data to ascertain that the behavior of the vehicle is as intended.

Intellectual property rights and their protection is a cornerstone of the automotive value chain. The automotive industry is composed by a meshwork of tightly integrated organizations that cooperate and compete in a hierarchical marketplace. Trading know-how and other virtual assets between participants is an essential part of this business. Thereby, software as a medium to transport ideas, innovations, and technologies plays a particular role. Protection of virtual goods and their associated rights is a current issue whose solution will determine how business will be done in the future automotive market. Automotive experts and researchers agree that ICT security technologies are a vital part to implement such a market. In this paper we examine the software life cycle of an automotive Electronic Control Unit (ECU) and discuss potential threats and countermeasures for each stage.