The Netcap (NETwork CAPture) framework efficiently converts a stream of network packets into highly accessible type-safe structured data that represent specific protocols or custom abstractions. These audit records can be stored on disk or exchanged over the network, and are well suited as a data source for machine learning algorithms. Since parsing of untrusted input can be dangerous and network data is potentially malicious, implementation was performed in a programming language that provides a garbage collected memory safe runtime. It was developed for a series of experiments in my bachelor thesis: Implementation and evaluation of secure and scalable anomaly-based network intrusion detection. Currently, the thesis serves as documentation until the wiki is ready, it is included at the root of this repository (file: mied18.pdf). Slides from my presentation at the Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities are available on researchgate.