Performance Comparison of Supervised Classifiers in Intrusion Detection Systems
Keywords:
Intrusion Detection System, Supervised Learning, Machine Learning, Classification, Deep LearningAbstract
Intrusion Detection System (IDS) is an essential component in network security to detect and respond to cyber attacks. This study explores the use of several supervised classifier algorithms to classify attacks using the KDDTest-21 dataset from NSL-KDD. This dataset was chosen because of its improvement over the KDD'99 dataset that reduces bias due to duplication and uneven data distribution. The algorithms used include Logistic Regression, KNearest Neighbors (KNN), Gaussian Naive Bayes, Support Vector Machine (SVM), Decision Treem, Random Fores and Deep Feedforward Neural Network. Each algorithm is applied to a normalized dataset using RobustScaler. Performance assessment is carried out based on metrics such as accuracy, precision, recall, and F1-score to determine
the best algorithm in detecting various types of attacks. The results show that several algorithms, especially Random Forest, have better performance in detecting attacks with high accuracy with 97.68% and other merics score are more than 98%. This finding is important for optimizing a more effective IDS in protecting network infrastructure from increasingly complex cyber attacks.
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