The term labeling refers to the procedure of adding classification information to each audit record. For the purpose of intrusion detection this is usually a label stating whether the record is normal or malicious. This is called binary classification, since there are just two choices for the label (good / bad). Another option is to use multi-class labels which could represent attack names or categories. Efficient and precise creation of labeled datasets is important for supervised machine learning techniques. To create labeled data, Netcap parses logs produced by suricata and extracts information for each alert. The quality of labels therefore depends on the quality of the used ruleset. In the next step it iterates over the data generated by itself and maps each alert to the corresponding packets, connections or flows. This takes into account all available information on the audit records and alerts. More details on the mapping logic can be found in the implementation chapter. While labeling is usually performed by marking all packets of a known malicious IP address, Netcap implements a more granular and thus more precise approach of mapping labels for each record. Labeling happens asynchronously for each audit record type in a separate goroutine.