Network Intrusion Classification on the UNSW-NB15 Dataset Using XGBoost Feature Selection Technique
Uday Chandra Akuthota, Lava Bhargava · 2023
Network intrusion has emerged as an essential issue for business and government societies in the cyber-threat environment. Implementing a system for detecting network intrusions has been recognized as crucial to distinguish between regular and anomalous network traffic to mitigate this potential risk. The efficiency of an intrusion detection system in a network is determined by its ability to identify potential threats as anomalies appropriately. This research primarily examines Intrusion Detection Systems (IDSs) constructed utilizing machine learning techniques. This study evaluates the UNSW-NB15 network intrusion dataset, which will be utilized for training and testing the algorithms. Additionally, a filter-based feature selection strategy is employed with the XGBoost algorithm. Next, we proceed to construct the following machine-learning algorithms, making use of the decreased feature space: Artificial neural network, k-Nearest Neighbour, and Decision Tree. Both binary and multiclass classifications are evaluated, and the results are compared with previous approaches.