Publication Type : Conference Paper
Publisher : Elsevier BV
Source : Procedia Computer Science
Url : https://doi.org/10.1016/j.procs.2023.01.226
Keywords : Network Intrusion Detection System, Resampling, Feature selection, Machine learning, Class Imbalance
Campus : Amaravati
School : School of Computing
Year : 2023
Abstract : Intrusion detection systems are a prominent field of research in order to identify attacks on computer networks. Data packets have many dimensions; therefore, examining them takes time. These dimensions include certain unimportant noises which are removed during pre-processing phase to increase the performance. Feature shuffle technique with random forest to calculate the roc-values of all the features used. A common machine learning algorithm for intrusion detection is used for implementation. Benchmark datasets CIC-DDoS2019 and Edge-IIoT were used to validate the proposed IDS. The experiment findings show that the proposed model is more accurate and has a higher Matthews Correlation Coefficient (MCC).
Cite this Research Publication : Bidyapati Thiyam, Shouvik Dey, Efficient Feature Evaluation Approach for a class-imbalanced dataset using Machine learning, International Conference on Machine Learning and Data Engineering, University of Petroleum and Energy Studies, Dehradun, , 2520-2532, , 2022-09- 07 https://doi.org/10.1016/j.procs.2023.01.226