Ensemble Machine Learning Techniques for Attack Prediction in NIDS Environment

Abstract

The need for network intrusion detection systems (NIDS) to protect against different attacks grows as the scale of cyber attacks increases. The main areas of cyber attack research are its detection and prevention. Traditional machine learning (ML) algorithms with low accuracy are used by the current NIDS, but it is not suitable for newer anonymous cyber attacks. In this paper, an NIDS model with ensemble ML methods, which can detect and prevent different types of attacks compared with traditional ML methods, is proposed. Our specific system detects known attacks and blocks unknown attacks. The selected system uses four different machine learning methods, including data processing techniques for data preprocessing and data labeling. The entire NSL-KDD database is used to evaluate the performance of various ML classifiers based on different parameters. The simulation analysis shows that the developed NIDS system is better than the existing single ML methods. The detection accuracy rate of intrusion detection system (IDS) is increased by the model, which is essential for NIDS.

Keywords

• Attacks
• Machine Learning
• Network intrusion detection systems
• NSL-KDD dataset
• Data Labelling