Zero-Touch Network Security (ZTNS): A Network Intrusion Detection System Based on Deep Learning

Abstract

The rapid evaluation of smart cities has revolutionized the research and development field to a very extensive level which presents challenges in handling massive amounts of data. However, the integration of IoT into various aspects of life has introduced various challenges related to the security and privacy of IoT systems. IoT sensors capture large volumes of sensitive customer data, which can potentially make them a target and pose serious threats, including financial loss and identity theft. Strong intrusion detection systems are essential for protecting networked, data-driven ecosystems from potential cyber threats. In this paper, we propose a novel deep learning-based approach that focuses on emerging zero-touch networks that autonomously manage network resources to ensure network security, the proposed approach identifies various network intrusions such as DDoS, Botnet, Brute force, and Infiltration. Our proposed approach presents a major improvement in IoT security. We have used the CICIDS-2018 benchmark dataset and propose a deep learning-based network intrusion detection System for Zero Touch Networks (DL-NIDS-ZTN). The proposed study utilizes convolutional neural networks that correctly identify benign and malicious traffic and achieve 99.80% accuracy with the CICIDS-2018 dataset. By implementing the DL-NIDS-ZTN methodology, we aim to strengthen the security framework of smart cities and ensure the secure and seamless integration of IoT.

Keywords

• Intrusion detection
• zero-touch networks
• smart city
• IoT
• deep learning
• convolutional neural networks