The public cloud is an alternative application development approach to traditional on-premises IT architectures. In the basic public cloud computing model, a third-party provider hosts scalable, on-demand IT resources and delivers them to users over a network connection, either over the public internet or a dedicated network. As an essential element of the next generation Internet, the internet of things (IoT) has been undergoing extensive development in recent years. In addition to the enhancement of people’s daily lives, IoT devices also gather a massive amount of data that could be utilized by machine learning and big data analytics for different applications. By connecting sensors, smart devices and everyday physical objects with the Internet, IoT provides new forms of communication for people and devices. This seamlessly integrates the virtual world of information with the real world. As many of these applications are related to a user’s daily life, privacy and security aspects are very important.

Unfortunately, the nature of the complex and heterogenous structure of IoT makes security issues very challenging. Artificial intelligence has now become essential to information security, as such technologies are capable of swiftly analyzing millions of data sets and tracking down a wide variety of cyber threats — from malware menaces to shady behavior that might result in a phishing attack. Artificial intelligence presents many advantages and has many applications in a variety of areas, including cybersecurity. With fast-evolving cyberattacks and the rapid multiplication of devices taking place currently, AI and machine learning can help to keep abreast of cybercriminals, automate threat detection, and respond more effectively than conventional software-driven or manual techniques. Security and authentication will continue to improve and become smarter. Eventually, authentication will likely move from supervised learning, where the dataset includes the outcomes, to unsupervised learning where AI finds new patterns that humans may not have discovered and makes predictions of potential factors to assess. Being able to cross reference multiple machine learning algorithms and use pattern recognition and time-series based predictive algorithms will improve the accuracy and scope of AI-based authentication offerings going forward, for web application logins, but also for other aspects of cybersecurity such as network intrusion and botnet detection. AI develops more effective algorithms to determine which factors indicate an attack by trying different techniques to solve problems and checking its answer against the answer in the dataset. Eventually, it finds a set of algorithms that enable it to accurately predict threats most of the time.

This special issue focuses on security and authentication for the public cloud in terms of theoretical and practical issues. The purpose of this special issue is to bring together researchers, industry personnel, academicians and individuals working in these areas and to exchange novel ideas and the latest findings. We welcome both original research and review papers.

Potential topics include but are not limited to the following:

• Artificial intelligence in secure and efficient privacy preserving set intersection with identity authentication in IoT
• Artificial intelligence in efficient privacy preserving anonymous authentication scheme for human predictive online education system
• AI in quantum secure authentication and key agreement multi agent interaction for public cloud
• Secure data sharing algorithm in privacy protection for IoT public cloud
• AI in efficient authenticated group key agreement protocol for dynamic UAV fleets in untrusted environments
• AI in edge computing based secure e-learning platforms
• AI in secure and reliable transfer learning framework for 6G enabled internet of vehicles
• AI in constructing trustworthy on blockchain enabled social credits system
• AI in privacy preserved, secure and mutually authenticated key agreement protocol for healthcare in public cloud
• AI in digital forensics for public cloud
• AI in edge computing for physical layer security