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In recent years, as intrusion attacks on IoT networks have grownexponentially, there is an immediate need for sophisticated intrusion detectionsystems (IDSs). A vast majority of current IDSs are data-driven, which meansthat one of the most important aspects of this area of research is the qualityof the data acquired from IoT network traffic. Two of the most cited intrusiondetection datasets are the KDDCup99 and the NSL-KDD. The main goal of ourproject was to conduct a robust comparison of both datasets by evaluating theperformance of various Machine Learning (ML) classifiers trained on them with alarger set of classification metrics than previous researchers. From ourresearch, we were able to conclude that the NSL-KDD dataset is of a higherquality than the KDDCup99 dataset as the classifiers trained on it were onaverage 20.18% less accurate. This is because the classifiers trained on theKDDCup99 dataset exhibited a bias towards the redundancies within it, allowingthem to achieve higher accuracies.

A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms