A methodology for conducting efficient sanitization of HTTP training datasets

The performance of anomaly-based intrusion detection systems depends on the quality of the datasets used to form normal activity profiles. Suitable datasets should include high volumes of real-life data free from attack instances. On account of this requirement, obtaining quality datasets from collected data requires a process of data sanitization that may be prohibitive if done manually, or uncertain if fully automated.