An enhanced J48 classification algorithm for the anomaly intrusion detection systems

In this paper, we have developed an enhanced J48 algorithm, which uses the J48 algorithm for improving the detection accuracy and the performance of the novel IDS technique. This enhanced J48 algorithm is seen to help in an effective detection of probable attacks which could jeopardise the network confidentiality. For this purpose, the researchers used many datasets by integrating different approaches like the J48, Naive Bayes, Random Tree and the NB-Tree. An NSL KDD intrusion dataset was applied while carrying out all experiments. This dataset was divided into 2 datasets, i.e., training and testing, which was based on the data processing. Thereafter, a feature selection method based on the WEKA application was used for evaluating the efficacy of all the features. The results obtained suggest that this algorithm showed a better, accurate and more efficient performance without using the above-mentioned features when compared to the feature selection procedure. An implementation of this algorithm guaranteed the dataset classification based on a detection accuracy of 99.88% for all the features when using the 10-fold cross-validation test, a 90.01% accuracy for the supplied test set after using the complete test datasets along with all the features and a 76.23% accuracy for supplying the test set after using the test-21 dataset along with all features.