A Comprehensive Analysis of Accuracies of Machine Learning Algorithms for Network Intrusion Detection

Abstract

Intrusion and anomaly detection are particularly important in the time of increased vulnerability in computer networks and communication. Therefore, this research aims to detect network intrusion with the highest accuracy and fastest time. To achieve this, nine supervised machine learning algorithms were first applied to the UNSW-NB15 dataset for network anomaly detection. In addition, different attacks are investigated with different mitigation techniques that help determine the types of attacks. Once detection was done, the feature set was reduced according to existing research work to increase the speed of the model without compromising accuracy. Furthermore, seven supervised machine learning algorithms were also applied to the newly released BoT-IoT dataset with around three million network flows. The results show that the Random Forest is the best in terms of accuracy (97.9121%) and Naïve Bayes the fastest algorithm with 0.69 s for the UNSW-NB15 dataset. C4.5 is the most accurate one (87.66%), with all the features considered to identify the types of anomalies. For BoT-IoT, six of the seven algorithms have a close to 100% detection rate, except Naïve Bayes.