SVM Based Network Intrusion Detection for the UNSWNB15 Dataset Due to the growth of internet security issues, Network Intrusion Detection System (NIDS) becomes an integral part of the IoT environment. In the past, most research on intrusion detection was experimented with the KDDCUP99 dataset. However, the KDDCUP99 dataset lacks some typical examples when evaluating NIDS compared with the UNSWNB15 dataset. In this paper, we propose Support Vector Machine (SVM) with a new scaling method for binaryclassification and multiclassification experiments. The performance of our method is evaluated through accuracy, detection rate and false positive rate. Compared with other methods, the superiority of the proposed SVM method is shown by the experimental results. The accuracy of the proposed method reaches 85.99% for binaryclassification, compared to 78.47% by ExpectationMaximization (EM) clustering. For multiclassification, the proposed SVM method can achieve the testing accuracy of 75.77%, which is 6.17% higher than that of Naive Bayes (NB).

A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets The problem of network intrusion detection poses innumerable challenges to the research community, industry, and commercial sectors. Moreover, the persistent attacks occurring on the cyberthreat landscape compel researchers to devise robust approaches in order to address the recurring problem. Given the presence of massive network traffic, conventional machine learning algorithms when applied in the field of network intrusion detection are quite ineffective. Instead, a hybrid multimodel solution when sought improves performance thereby producing reliable predictions. Therefore, this article presents an ensemble model using metaclassification approach enabled by stacked generalization. Two contemporary as well as heterogeneous datasets, namely, UNSW NB15, a packetbased dataset, and UGRxe2x80x9916, a flowbased dataset, that were captured in emulated as well as real network traffic environment, respectively, were used for experimentation. Empirical results indicate that the proposed stacking ensemble is capable of generating superior predictions with respect to a realtime dataset (97% accuracy) than an emulated one (94% accuracy).