On the educated selection of unsupervised algorithms via attacks and anomaly classes

Anomaly detection aims at finding patterns in data that do not conform to the expected behavior. It is largely adopted in intrusion detection systems, relying on unsupervised algorithms that have the potential to detect zero-day attacks; however, efficacy of algorithms varies depending on the observed system and the attacks. Selecting the algorithm that maximizes detection capability is a challenging task with no master key. This paper tackles the challenge above by devising and applying a methodology to identify relations between attack families, anomaly classes and algorithms. The implication is that an unknown attack belonging to a specific attack family is most likely to get observed by unsupervised algorithms that are particularly effective on such attack family. This paves the way to rules for the selection of algorithms based on the identification of attack families. The paper proposes and applies a methodology based on analytical and experimental investigations supported by a tool to i) identify which anomaly classes are most likely raised by the different attack families, ii) study suitability of anomaly detection algorithms to detect anomaly classes, iii) combine previous results to relate anomaly detection algorithms and attack families, and iv) define guidelines to select unsupervised algorithms for intrusion detection.