Application of Machine Learning Classifiers Interfacing Google Colab and Sklearn to Intrusion Detection CSE-CIC-IDS2018 Dataset
Morgan L. Smith, Tor A. Kwembe · 2023
Network Intrusion Detection System (IDS) refers to the ability to quickly view network data information, identify any patterns of infiltration, and stop any detrimental effects of abnormal intrusion that could destroy the network. In this concept paper, we have suggested an IDS based on supervised stack ensemble machine learning classification techniques to address this problem. The system is capable of detecting a variety of network threats, according to the findings of using the IDS on the CICIDS2018 data from the University of New Brunswick and the Canada Institute of Cybersecurity (CIC). The proposed IDS model is design to run within the Google Colaboratory (Colab) integrated developmental environment (IDE). A dimension reduction technique approach is also incorporated to enhance the predictive accuracy of the chosen machine learning classification techniques from the Sklearn function libraries.