Exploring the NSL-KDD Dataset: A Comprehensive Analysis about Intrusion Detection System (IDS)

In this research article the researcher emphasized the Network threats and hazards are evolving at a high-speed rate in recent years. Many mechanisms (such as firewalls, anti-virus, anti-malware, and spam filters) are being used as security tools to protect networks. An intrusion detection system (IDS) is also an effective and powerful network security system to detect unauthorized and abnormal network traffic flow. This article presents a review of the research trends in network-based intrusion detection systems (NIDS), their approaches, and the most common datasets used to evaluate IDS Models. The analysis reported presented in this paper is based on the supervised machine learning approach logistics and XGB- classifier by using NSL-KDD Dataset. The researcher found that logistic classifier given 0.95% accuracy where as XGBooster Classifier gives the 1.00% accuracy , due to the over fitting the researcher used the hyper parameter tuning XGB classifier and got the 0.99% accuracy. The researcher assured that the developed predictive model is more accurate and efficient to detect the intrusion during the data transmission.