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Complex and new cases of intrusions, new bugs, security issues and vulnerabilities are evolving everyday for a number of reasons. Consequently, researchers in the domains of Intrusion Detection Systems and Intrusion Prevention Systems constantly design new methods to lessen the aforementioned security issues. However, getting suitable datasets for evaluating various research designs in these domains is a major challenge for the research community, vendors and data donors over the years. As a result, most intrusion detection and prevention methodologies are evaluated using wrong categories of datasets because the limitations of each category of evaluative datasets are unknown. Therefore, this paper presents a critique of the challenges associated with evaluative datasets for investigating intrusion detection and prevention methodologies and how these challenges can be lessened. Finally, these analyses will effective guide researchers and vendors in securing evaluative datasets for validating the intrusion detection and prevention systems.
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Joshua Ojo Nehinbe
Edo State University Uzairue
University of Essex
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Joshua Ojo Nehinbe (Thu,) studied this question.
synapsesocial.com/papers/6a0f06031c5e2d2319fa3eba — DOI: https://doi.org/10.1109/cis.2011.6169141
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