Statistical analysis reveals critical evaluation flaws in the KDD CUP 99 benchmark dataset, prompting the creation of the refined NSL-KDD dataset to improve anomaly detection testing.
During the last decade, anomaly detection has attracted the attention of many researchers to overcome the weakness of signature-based IDSs in detecting novel attacks, and KDDCUP'99 is the mostly widely used data set for the evaluation of these systems. Having conducted a statistical analysis on this data set, we found two important issues which highly affects the performance of evaluated systems, and results in a very poor evaluation of anomaly detection approaches. To solve these issues, we have proposed a new data set, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.
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Tavallaee et al. (2009) studied this question.
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