Optimized Network Intrusion Prevention System Via Dragonfly Algorithm-Based Feature Selection

The advancements in network technology and associated services have increased the volume of data traffic. However, the harmful impact caused by intruders has also increased. Intrusion activities can be addressed by utilizing Network Intrusion Prevention Systems (NIPS). NIPS can detect attacks by either comparing the traffic with predefined patterns or identifying deviations from normal traffic behavior. In this paper, the machine learning algorithms will be used to enhance the performance of the NIPS systems. First, feature selection algorithms will be used to remove irrelevant features from the network traffic data. In particular, the widely used Dragonfly Algorithm (DA) will be used for feature selection. Then, the Gradient-Boosted Trees (GBT) and Logistic Regression (LR) will be used to find the attack data based on the features selected by the DA algorithm. The UNSWNB15 dataset was used to assess the proposed NIPS system. The accuracy of GBT and LR reaches 100 % and 99.93 %, respectively.