Open Access

Kevin Wallis, Fabian Schillinger, Christoph Reich, Christian Schindelhauer

• Ensuring data quality is central to the digital transformation in industry. Business processes such as predictive maintenance or condition monitoring can be implemented or improved based on the available data. In order to guarantee high data quality, a single data validation system are usually used to validate the production data for further use. However, using a single system allows an attacker only to perform one successful attack to corrupt the whole system. We present a new approach in which a data validation system using multiple different validators minimizes the probability of success for the attacker. The validators are arranged in clusters based on their properties. For a validation process, a challenge is given that specifies which validators should perform the current validation. Validation results from other validators are dropped. This ensures that even for more than half of the validators being corrupted anomalies can be detected during the validation process.