Predictive Model for Network Intrusion Detection System Using Deep Learning

Given the recent COVID-19 situation, many organizations and companies have asked their employees to work from home by connecting to their on-premises servers This situation may continue a much more extended period in the future, thereby opening more threats to confidentiality and security to the information available in the organizations It becomes of hell of a task for network administrators to counter the threats Intrusion Detection Systems are deployed in firewalls to identify attacks or threats In preset modern technologies, Network Intrusion Detection System plays a significant role in defense of the network threat Statistical or pattern-based algorithms are used in NIDS to detect the benign activities that are taking place in the network In this work, deep learning algorithms have developed in NIDS predictive models to detect anomalies and threats automatically Performance of the proposed model assessed on the NSL-KDD dataset in the view of metrics such as accuracy, recall, precision, and F1-score The experimental results show that the proposed deep learning model outperforms when compared with existing shallow models © 2020 Lavoisier All rights reserved