  Anomaly detection systems need to consider a lot of information when scanning
for anomalies. One example is the context of the process in which an anomaly
might occur, because anomalies for one process might not be anomalies for a
different one. Therefore data -- such as system events -- need to be assigned
to the program they originate from. This paper investigates whether it is
possible to infer from a list of system events the program whose behavior
caused the occurrence of these system events. To that end, we model transition
probabilities between non-equivalent events and apply the $k$-nearest neighbors
algorithm. This system is evaluated on non-malicious, real-world data using
four different evaluation scores. Our results suggest that the approach
proposed in this paper is capable of correctly inferring program names from
system events.
