Internet of Things (IoT) allows billions of devices in the physical world as well as virtual environments to exchange data with each other intelligently. For example, smartphones have become an important personal assistant and an indispensable part of people's everyday life and work.

Machine learning has now been widely applied to IoT in order to facilitate performance and efficiency, such as reinforcement learning and deep learning. However, machine learning also suffers many issues, which may threaten the security, trust, and privacy of IoT environments. Among these issues, adversarial learning is one major threat, in which attackers may try to fool the learning algorithm with particular training examples, and lead to a false result.

This Special Issue will focus on cutting-edge research from both academia and industry and aims to solicit original research and review articles with a particular emphasis on discussing the security, trust, and privacy challenges in machine learning-based IoT.

Potential topics include but are not limited to the following:

• Machine learning-based intrusion detection
• Privacy attacks including machine learning-based attacks
• Secure data collection with machine learning-based IoT
• IoT privacy and anonymity with machine learning - forensics techniques
• Trust management with machine learning for IoT applications
• Applications of machine learning in IoT security, trust, and privacy
• Vulnerability assessment in machine learning-based IoT
• Secure routing in machine learning-based IoT
• Adversarial learning for IoT