To detect deviations in all feature values, we use an isolation forest algorithm. In theory, clustering techniques, e.g. clustering techniques for large data sets, may be suited for the same task. However, we encounter many drawbacks using clustering algorithms on a large data set which follows power law distributions. Only naming few: the heterogeneity in the data density leads to especially uneven cluster sizes, distance metrics do not support odd shapes, and there are difficulties finding appropriate mesh sizes for heterogenic data densities for using grid-based clustering approaches. We argue that an isolation forest algorithm is well suited because no distance metric is used and the algorithm can take advantage of the heterogenic data density, as anomalous samples are detected easier. The algorithm works on an ensemble of isolation trees, where each branch randomly selects a feature and a feature value between its minimum and its maximum. The decision tree is divided until one data point is isolated from the rest. The average number of branches, in other words the average length of the trees in the forest, is used to characterise the sample.