An investigation of the Intrusion detection system for the NSL-KDD dataset using machine-learning algorithms

Over the last few years, the use of an Intrusion Detection System (IDS) has proven to be an effective method for achieving higher levels of security by detecting potentially harmful actions. Because it is unable to accurately identify all types of attacks, the current method of anomaly detection is frequently associated with high rates of false alarms and low rates of accuracy and detection. When it comes to establishing reliable and all-encompassing security, intrusion detection systems (IDS) are invaluable tools for managed service providers (MSPs). Since there are so many IDS options, it can be hard to figure out which one is best for your business and your customers. When it comes to training and testing an IDS, having access to a dataset with a large amount of high-quality data representative of real-world conditions is invaluable. In this work, NSL-KDD dataset is analyzed and is used to assess the effectiveness of various classification algorithms in detecting anomalies in network traffic patterns. In addition, we investigate the relationship between hacker attacks and the protocols found in the commonly used network protocol stack. These investigations were carried out to determine how attackers generate abnormal network traffic. The investigation has yielded a wealth of information about the relationship between the protocols and network attacks. Furthermore, the proposed model not only improves IDS precision but also opens up a new research avenue in this field.