The main objective of this study is not to identify the best machine learning model, but instead to review the main datasets, publicly available, used to train and test security solutions that employ modern classification algorithms for anomaly detection. Hence, DARPA 1998 and KDD were studied as they were the first initiatives taken in this direction, while NSL-KDD, ISCXIDS2012 and CICIDS2017 are taken in consideration for future research because of their advantages. Personalized datasets will always bring a reasonable amount of uncertainty, especially since some feature vectors used for training remain unknown. Nevertheless, training on data specific to the protected infrastructure is more efficient, from the security point of view, than training on old attack signatures.
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