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Most research in the field of network intrusion detection heavily relies on datasets. Datasets in this field, however, are scarce and difficult to reproduce. To compare, evaluate, and test related work, researchers usually need the same datasets or at least datasets with similar characteristics as the ones used in related work. In this work, we present concepts and the Intrusion Detection Dataset Toolkit (ID2T) to alleviate the problem of reproducing datasets with desired characteristics to enable an accurate replication of scientific results. Intrusion Detection Dataset Toolkit (ID2T) facilitates the creation of labeled datasets by injecting synthetic attacks into background traffic. The injected synthetic attacks created by ID2T blend with the background traffic by mimicking the background traffic’s properties.

This article has three core contributions. First, we present a comprehensive survey on intrusion detection datasets. In the …