An Innovative Multi-Dataset Performance Analysis of Machine Learning Classifiers based on Features Reduction for Intrusion Detection - Mohammed, Salim Q.; Hussein, Mohammed A. El Sheikh (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Computer users receive millions of internet packets every day, some are regular normal usage packets, others are packets sent by intruders for illegal purposes. With the increased numbers of users, regular countermeasure methods are no longer effective and Machine Learning is a key tool to deal with this increase of user numbers and attack types. Three well-known datasets, KDD99, UNSW NB15, and CICIDS2017 are used as a framework for a comprehensive comparison study were the proposed models deployed for performances measurements using a number of features reduction methods. Nine machine learning models that start with K-Nearest Neighbor, Logistic Regression, Support Vector Machine-Linear, Stochastic Gradient Descent, Nave Bayes, Decision Trees, Random Forest, Gradient Boosting to Adaboost are applied to the reduced features datasets of KDD99, UNSW NB15, and CICIDS2017. For a variety of features reductions, the accuracy and F1 score metrics have been used to evaluate and analyze each model’s performance. For KDD99, the achieved accuracy and F1 scores are 99.9663% and 99.979%, respectively, and with the UNSW NB15, the values are 95.1968% and 96.2473%, respectively. Finally, the CICIDS2017 dataset values of 99.7004% for accuracy and 99.7515% for F1 were obtained. Random forest classifier showed the highest performances values using all the three datasets, and the innovative features reduction by 80% gave better outcomes of accuracy and F1, surpassing other state-of-the-art surveyed researches.