This research aims to assist the developers of intrusion detection systems (IDS) to make the right selection decision of a suitable classification model. Many classification algorithms have been developed to be used in an IDS detection engine. Developers of IDS have been facing challenges in how to evaluate and benchmark classifiers. Different perspectives and multiple, conflicting importance evaluation criteria represent the challenges in evaluation, benchmarking and selecting suitable IDS classifiers. The current evaluation studies depend on evaluating the IDS classifier from a single incomplete perspective. In each study, the evaluations have been achieved with reference to some security-related evaluation criteria and ignore performance criteria. Furthermore, the weighting process that reflects the importance of each criterion depended on a personal subjective perspective. The goal of this thesis is to set a new standardisation and benchmarking framework based on a set of.....

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