Dear Colleagues,

Emerging as the central building block of future networks, 5G and beyond mobile networks have shown the potential to support billions of mobile devices. Their boom, however, comes with the risk of being more susceptible to security threats, also imposing security challenges to networking technologies such as software-defined networking (SDN), network function virtualization (NFV), the Internet of Things (IoT) and mobile edge computing (MEC). Traditional security techniques may be insufficient, as they have the potential to fail to meet requirements such as ultra-low latency and deterministic properties. In addition, they may no longer be applicable, as cyberattacks have evolved with 5G and beyond networks, prompting unprecedented security risks.

Currently, machine learning (ML) technology is used in both industry and academia due to its data-driven feature for realizing high-performance networks. ML algorithms can be applied throughout networks to predict potential problems without the need of any external resources, making it a promising technique in detecting attacks in 5G and beyond networks, e.g., ones which are similar to known attacks but that are hard to detect through the use of traditional algorithms. More importantly, ML in 5G and beyond security brings novel possibilities in analyzing, modeling and detecting network threats, whilst also enabling the learning of unprecedented attacks in emerging 5G and beyond networks, so that they can be detected and addressed without the need for external resources.

The purpose of this Special Issue is to provide a premier forum for researchers and academics working on ML in 5G and beyond security to present their state-of-the-art research contributions. We invite submissions of high-quality papers in the form of either original research or surveys/overviews, which have not been published previously and are not currently in press, under review or being considered for publication by another journal or conference. Potential topics include, but are not limited to, the following:

• ML in 5G and beyond security;
• ML-driven attack model generation and specification;
• ML for big data security/cloud security/IoT security;
• ML-based cryptographic protocols in networks;
• ML-based identity management in 5G and beyond networks;
• ML for predicting user behavior in 5G and beyond networks;
• the analysis, modelling and visualization of attacks using ML;
• security threats, intrusions and malware detection exploiting ML methods;
• security architectures for heterogeneous 5G and beyond mobile networks;
• trusted and trustworthy computing for 5G and beyond mobile technologies;
• the orchestration of SDN or NFV for securing 5G mobile applications;
• data security for 5G device-to-device (D2D) communications;
• secure mobile augmented reality (AR)/virtual reality (VR) applications with 5G and beyond networks.

Prof. Dr. Yuanlong Cao
Dr. Bo Wei
Dr. Shuai Zhao
Guest Editors

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• 5G and beyond security
• machine learning
• data security
• trusted computing
• secure mobile augmented reality (AR)/virtual reality (VR)