Identifying attack traffic is very important for the security of Internet of Things (IoT) in smart cities by using Machine Learning (ML) algorithms. Recently, the IoT security research community has endeavoured to build anomaly, intrusion, and cyber-attack traffic identification models using Machine Learning algorithms for IoT security analysis.

However, some critical and significant problems have not yet been studied in depth. One such problem is how to select an effective ML algorithm when there are numbers of ML algorithms for a cyber-attack detection system for IoT security. Will early stage traffic management give effective results if applied to IoT traffic management by using ML algorithms, or will this affect the performance of the ML model if several features are selected? Methods must avoid the risk of inaccuracy, inefficiency, and privacy leakage of machine learning techniques in IoT.

The main objective of this Special Issue is to publish articles based on feature selection, algorithms, protocols, frameworks, and machine learning techniques in IoT that extend the current state of the art with innovative ideas and solutions in the broad area of security attacks traffic detection and network traffic management. Theoretical as well as experimental studies for typical and newly emerging convergence technologies and cases enabled by recent advances are encouraged. High-quality review papers are also welcome.

Potential topics include but are not limited to the following:

• Security attack traffic identification using machine learning techniques
• IoT network traffic management
• New concepts and architectures for IoT traffic management
• Malicious traffic identification in IoT network
• Quality of Experience (QoE)/Quality of Service (QoS) for IoT network management
• Security strategy in IoT systems using machine learning techniques
• ML algorithms for IoT applications
• Security and privacy techniques in IoT environment
• Smart IoT devices selection in IoT network using machine learning techniques
• Security-related classification and analytics
• Blockchain-based security management for IoT applications
• New concepts and architecture for IoT traffic management
• Malicious behaviour identification for IoT network using machine learning techniques
• Storage issues in IoT networks