In practice, JStap outperforms existing systems, which we reimplemented and tested on our dataset totaling over 270,000 samples. To improve the detection, we also combine the predictions of several modules. A first layer of unanimous voting classifies 93% of our dataset with an accuracy of 99.73%, while a second layer–based on an alternative modules’ combination–labels another 6.5% of our initial dataset with an accuracy over 99%. This way, JStap can be used as a precise pre-filter, meaning that it would only need to forward less than 1% of samples to additional analyses. For reproducibility and direct deployability of our modules, we make our system publicly available.