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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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Mahbod Tavallaee
Ebrahim Bagheri
Wei Lu
National Research Council Canada
National Academies of Sciences, Engineering, and Medicine
University of New Brunswick
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Tavallaee et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d73fc8c74376700bf31132 — DOI: https://doi.org/10.1109/cisda.2009.5356528
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