Intrusion Detection System Using Deep Learning

Abstract

With recent advancements in computer network technologies, there has been a growth in the number of security issues in networks. Intrusions like denial of service, exploitation from inside a network, etc. are the most common threat to a network's credibility. The need of the hour is to detect attacks in real time, reduce the impact of the threat, and secure the network. Recent developments in deep learning approaches can be of great assistance in dealing with network interference problems. Deep learning approaches can automatically differentiate between usual and irregular data with high precision and can alert network managers to problems. Deep neural network (DNN) architectures are used with differing numbers of hidden units to solve the limitations of traditional ML models. They also seek to increase predictive accuracy, reduce the rate of false positives, and allow for dynamic changes to the model as new research data is encountered. A thorough comparison of the proposed solution with current models is conducted using different evaluation metrics.