A framework for the evaluation of intrusion detection systems

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Security Evaluation of Pattern Classifiers under Attack (2014, methodology): “An intrinsic limitation of our work is that security evaluation is carried out empirically, and it is thus datadependent; on the other hand, model-driven analyses [12], [17], [38] require a full analytical model of the problem and”
A Survey of Physics-Based Attack Detection in Cyber-Physical Systems (2018, background): “…curves, most popular metrics for evaluating the classification accuracy of intrusion detection systems can be shown to be a multi-criteria optimization problem between the false-alarm rate, and the true positive rate [14], and all of them depend on the ability of a system to detect some attacks.”
A detailed analysis of the KDD CUP 99 data set (2009, methodology): “Besides, the performance measure applied in DARPA’98 Evaluation, ROC Curves, has been widely criticized, and since then many researchers have proposed new measures to overcome the existing deﬁciencies [8], [9], [10], [11], [12].”
Limiting the Impact of Stealthy Attacks on Industrial Control Systems (2016, background): “Most intrusion detection metrics are variations of the fundamental trade-off between false alarms and true positive rates [11], however, our adversary by definition will never be detected so we cannot use true positive rates (or variations thereof).”