***Dataset Information*** KDDTrain+.ARFF The full NSL-KDD train set with binary labels in ARFF format KDDTrain+.TXT The full NSL-KDD train set including attack-type labels and difficulty level in CSV format The full NSL-KDD train set including attack-type labels and difficulty level in CSV format KDDTrain+_20Percent.ARFF A 20% subset of the KDDTrain+.arff file KDDTrain+_20Percent.TXT A 20% subset of the KDDTrain+.txt file KDDTest+.ARFF The full NSL-KDD test set with binary labels in ARFF format KDDTest+.TXT The full NSL-KDD test set including attack-type labels and difficulty level in CSV format KDDTest-21.ARFF A subset of the KDDTest+.arff file which does not include records with difficulty level of 21 out of 21 KDDTest-21.TXT A subset of the KDDTest+.txt file which does not include records with difficulty level of 21 out of 21 ***Improvements to the KDD'99 data set *** The NSL-KDD data set has the following advantages over the original KDD data set: It does not include redundant records in the train set, so the classifiers will not be biased towards more frequent records. There is no duplicate records in the proposed test sets; therefore, the performance of the learners are not biased by the methods which have better detection rates on the frequent records. The number of selected records from each difficultylevel group is inversely proportional to the percentage of records in the original KDD data set. As a result, the classification rates of distinct machine learning methods vary in a wider range, which makes it more efficient to have an accurate evaluation of different learning techniques. The number of records in the train and test sets are reasonable, which makes it affordable to run the experiments on the complete set without the need to randomly select a small portion. Consequently, evaluation results of different research works will be consistent and comparable.