A New Intrusion Detection Scheme Using CatBoost Classifier

Advancements in the network infrastructure have caused a positive influence in our day to day life. Many reform initiatives have been taken all over the world which are related to the digitization of the countries methodologies of handling information. The usage of modern techniques also has a drawback, which allows data theft. Hence, a secure system is required, which can detect any kind of fraudulent activity and alert the administrator. Such a system is called an Intrusion Detection System (IDS). There are many types of IDSs available at our disposal, and a lot of research has also been done on their various types. This paper presents the implementation of IDS based on CatBoost technique which is a part of the ensemble machine learning strategy. The results of the implementation have been evaluated on the evaluation metrics like accuracy, precision, recall, and F1-score. The programming environment used is Python. The implementation has experimented on the NSL-KDD dataset, and the results have been analyzed on the detection accuracy, which shows the proposed scheme has reached an accuracy of 99.46% on the NSL-KDD dataset.