Enhancing network intrusion detection systems with combined network and host traffic features using deep learning: deep learning and IoT perspective
Estabraq Saleem Abduljabbar Alars, Sefer Kurnaz
Abstract
Network security is a key concern in today’s linked world as cyber threats grow more sophisticated and ubiquitous. Traditional Network Intrusion Detection Systems (NIDS) generally fall short owing to their dependence on predetermined signatures and restricted detection scope, exposing substantial gaps in efficiently recognizing new and unanticipated intrusions. This research tackles these difficulties by merging network and host traffic data with sophisticated deep learning algorithms to boost NIDS performance. Utilizing the Network Intrusion Detection dataset, which comprises multiple intrusion scenarios replicated in a military network context, our technique involves painstaking data collection, preprocessing, and feature extraction. We employed a convolutional neural network (CNN) to assess these data, applying rigorous feature selection and dimensionality reduction to enhance model performance. The findings reveal that our deep learning-based NIDS achieves an amazing detection accuracy of 98.5%, exceeding current approaches and successfully resolving real-world cybersecurity problems. This complete approach not only develops NIDS technology but also provides a practical solution for boosting network security across many applications, therefore contributing to the development of intrusion detection systems.