Comparative Analysis of ML Algorithms for Detecting Intrusion Using the NSL-KDD Dataset
Ravindra Singh Koranga, Arun Kumar, Shobhit Kumar, Hradesh Kumar · 2025
With the advent of computer networks, it has become very convenient to communicate, share and collaborate with others in a short time. All the online services depend on computer networks. With the rise of computer networks there is a increase in threats of cyberattacks. Intrusion by an attacker is very common which can put the whole network at risk. Thus Intrusion detection has become very important today. There are several traditioanl methods which can indetify the intrusion. This study focuses on the use of Machine Learning to detect intrusion. Several machine learning models are trained on the NSL-KDD dataset from Kaggle. This dataset contains several factors which can result in intrusion. These factors are input to Machine Learning model to make prediction about any attack in the network. A correlation analysis of these factors is also done to identify the relationships among these factors. The performance of several Machine Learning algorithms are analysed based on several parameters and the best model is determined.