Seeing is understanding: anomaly detection in blockchains with visualized features

Modern IoT solutions are often an intricate system of interdependent components. Traditional monitoring techniques may not be sufficient to ensure the correct operation of those systems. We present an on-line machine learning approach for anomaly detection that is optimized for interpretability. The aim is to make it as intuitive as possible for human operators to derive insights about the system. To this end we combine characteristics of the system into sets of features that can be rendered graphically. Our solution builds on open source components and applies to any time series of numerical data. This work originated within a larger project on connected mobility that uses Blockchain technology to guarantee data integrity. Hence we demonstrate some results at the example of the public Ethereum blockchain. Further work will extend the solution to more general sensor data from the IoT realm.