An ANN Based Sequential Detection Method for Balancing Performance Indicators of IDS In recent years, the number of cyber attacks has been increasing rapidly and network security has become an important issue. As a vital component of defense against network threats, intrusion detection system (IDS) was introduced and machine learning algorithms have been widely used in such systems for high detection performance. There are several evaluation indices such as false positive rate, false negative rate, and so on. A problem is that these indices are often related to each other. For example, while we try to decrease the false positive rate, the false negative rate often tends to increase, and vice versa. In this study, we proposed an ANN based sequential classifier method to mitigate this problem. Specifically, we try to train ANN to have a low false positive rate, despite which may lead to high false negative rate. Then, the reported negative instances are sent to the next ANN to make a further investigation, where the false negative instances reported at the previous ANN may be classified correctly. In this way, the final false negative rate can also be improved greatly. The results of the experiment shows that the proposed method can bring lower false negative rate and higher accuracy of detection while making the false positive rate at an acceptable level. Moreover, the optimum number of ANNs for our proposal is also investigated and discussed in this study.

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).