Securing massive IoT in 6G: Recent solutions, architectures, future directions

Abstract

The Internet of things (IoT) is a fast evolving technology that enables the development of smart services in numerous domains, such as transportation, health care, and smart cities. Such plethora of IoT devices is expected to be impact sixth-generation (6G) networks. Securing 6G/massive IoT networks against threats, especially novel attacks, is a major challenge for 6G infrastructure to accommodate. Thus, new innovative architectures and paradigms empowered by intelligence, softwarization, and infrastructure virtualization are urgently needed. This manifestation depends upon the power of artificial intelligence integrated with key networking enablers such as software-defined networking (SDN) and network functions virtualization (NFV) etc. to provide components of promising solutions and innovative approaches for securing the IoT. Researchers across the spectrum in computer science and engineering are striving to employ machine learning (ML) and deep learning (DL) techniques to improve the detection of cyber threats so that they can classify, predict, and mitigate anomalies and abnormal traffic behaviors. The challenge to design an intrusion detection system (IDS) is how to come up with a scalable solution within a realistic infrastructure architecture aimed at dealing with massive IoT applications. There is a multiplicity of factors to consider toward an optimal intelligent solution, however, we can perceive that future solutions should be tailored on native virtualized infrastructure. In this survey, we provide a comprehensive study on massive IoT security aspects toward 6G networks, especially IDS systems. To the best of our knowledge, this is the first survey that combines the integration of ML/DL and key networking technology enablers that support future 6G infrastructure for massive IoT security, with the aim to outline research challenges, relevant issues, and future directions.