CopulaGAN Boosted Random Forest based Network Intrusion Detection System for Hospital Network Infrastructure

The growing dependence on technology in healthcare has resulted in the creation of sophisticated hospital networks that are highly linked and vulnerable to cyber threats. A reliable Network Intrusion Detection System (NIDS) is required to identify and prevent such cyberattacks. The network intrusion detection is vital for safeguarding hospital networks and guaranteeing data security. The CICIDS2017 dataset contains a comprehensive set of network traffic characteristics for assessing network intrusion detection systems. Besides that, class imbalance is a prevalent difficulty in intrusion detection and it may have a considerable impact on the effectiveness of classification algorithms. The suggested solution employs a Machine Learning (ML) based NIDS for hospital networks that utilizes CopulaGAN (Generative Adversarial Network) to address the challenges due to imbalanced class ratio. The synthetic samples of minority classes were created to balance the dataset and improve detection accuracy. The Random Forest (RF) algorithm is used to discover the most defining features in the dataset and its hyperparameters are tuned to improve classification performance. Overall, the CopulaGAN boosted Random Forest based NIDS described here is a valuable solution for detecting network intrusions in hospital networks.