Efficient Feature Evaluation Approach for a class-imbalanced dataset using Machine learning

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).