The Efficiency of Ensemble Machine Learning Models on Network Intrusion Detection using KDDCup 99 Dataset
Artificial Neural Network(ANN); Decision Tree (DT); Gradient Boosting (GB); Logistic Regression (LR); Nae Bayes (NB); Network Intrusion Detection System (NIDS); Random Forest (RF); Support Vector Machine (SVM)
With the advent of data communication the increased usage of the technologies results in network intrusions and associated attacks. Consequently, the data violation rates are increased abundantly and that sacrifices Confidentiality, Integrity and Availability. This article focused on the network Intrusion Detection System (IDS) that detects various attacks and types. Machine learning (ML) has the potential to spot known-experience and Zero-day attacks. Consequently, the article has considered ML and ensembled models for the various attack classification. The major contributions of the current article are 3-fold. Initially, to understand the relevance and sufficiency of the dataset through exploratory data analysis. Second, the comprehensive understanding of the various attacks, its nature, various types and classifications and finally, the empirical analysis of the dataset through the potential of various ML models. The article utilized various discriminative models for the execution and all of the models have shown better accuracy. The tree-based ensemble model, Random Forest has outperformed the rest of the models with higher accuracy in the training and testing samples of 99.997 % and 99.969 % respectively. 2023 IEEE.
Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
Institute of Electrical and Electronics Engineers Inc.
Varghese N., CHRIST (Deemed to Be University), Department of Computer Science, Bangalore, India; Vivek R., Krupanidhi Degree College, BCA Department, Bangalore, India