Enhancement of Accuracy and Performance of Deep Learning System for Intrusion Detection System

In post pandemic scenario, most studies have been realized that there is a need for more work on accuracy in IDS, taking into account the prior study in the field. Furthermore, various aspects are impacting the time consumption during training operations in existing literature. Conventional studies have only given a few solutions for effective intrusion detection. When used, the insights and recommendations from this study will have a significant effect on the strategy employed to reliably foretell IDS. Taking into consideration the training model, the current research ought to provide a flexible and scalable approach to IDS detection. The proposed model will train on a large dataset, increasing the likelihood that it would provide accurate results. Future research should continue utilizing the same paradigm in order to enhance IDS detection. The finding has significant implications for improving IDS forecasting