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With the increasing amount of reliance on digital data and computer networksby corporations and the public in general, the occurrence of cyber attacks hasbecome a great threat to the normal functioning of our society. Intrusiondetection systems seek to address this threat by preemptively detecting attacksin real time while attempting to block them or minimizing their damage. Thesesystems can function in many ways being some of them based on artificialintelligence methods. Datasets containing both normal network traffic and cyberattacks are used for training these algorithms so that they can learn theunderlying patterns of network-based data. The CIDDS-001 is one of the mostused datasets for network-based intrusion detection research. Regarding thisdataset, in the majority of works published so far, the Class label was usedfor training machine learning algorithms. However, there is another label inthe CIDDS-001, AttackType, that seems very promising for this purpose andremains considerably unexplored. This work seeks to make a comparison betweentwo machine learning models, K-Nearest Neighbours and Random Forest, which weretrained with both these labels in order to ascertain whether AttackType canproduce reliable results in comparison with the Class label.

Machine Learning for Network-based Intrusion Detection Systems: an Analysis of the CIDDS-001 Dataset