The 10th International Workshop on Security, Privacy, Trust, and Machine Learning for Internet of Things (IoTSPT-ML 2020) conjunction with the 29th International Conference on Computer Communications and Networks (ICCCN 2020) http://www.google.com/url?q=http%3A%2F%2Fwww.icccn.org%2F&sa=D&sntz=1&usg=AFQjCNFiSauRNkwKn49E4pvsIhgnTytstQ, in Honolulu, Hawaii, USA. https://sites.google.com/uw.edu/iotspt-ml-2020) will be held in All papers presented in IoTSPT-ML 2020 will be published in the workshop proceedings. Outstanding papers will be invited to extend to full version for a SCI(E)-indexed journal, which is currently under contact. Call for Papers Experts predict that there will be 3-4 billion of connected devices in use by consumers by the end of this year. Although these devices in smart TVs, microwave ovens, thermostats, etc., will probably make our lives more energy and cost efficient, they can also threaten the security of our homes. This is because the manufacturers of these devices are primarily interested in functionality and do not focus on securing the device against cyber-attacks, protecting the privacy of consumer information on the device, securing the communications from/to the device, etc. The massive scale and the variety of these devices also make it difficult for the manufacturers to design and implement manageable security and privacy solutions. Another challenge in the IoT world is the continuous collection of data from the devices which is analyzed to make conclusions about the environment being monitored by the IoT devices. The data analyses are also crucial to maintaining the security and privacy of the data being collected from the devices. The massive scale of next-generation IoT systems makes the data collection, analyses, transport, and fusion of the results at the system level seem daunting. This workshop aims to promote discussions of research and relevant activities in the models and design of secure, privacy-preserving, or trust architectures, data analyses and fusion platforms, protocols, algorithms, services, and applications for next generation IoT systems. We especially encourage security and privacy solutions that employ innovative machine learning techniques to tackle the issues of data volume and variety problems that are systemic in IoT platforms. We plan to seek previously unpublished work in theoretical or experimental research, or work in-progress on topics including, but not limited to, the following: * Architectures and protocols for scalable, secure, robust and privacy enhancing IoT * Security and privacy frameworks for IoT * Cryptographic approaches for security and privacy in IoT * Trust frameworks and management models for IoT * Wireless security protocols for IoT * Threat and attack models in IoT * Intrusion and malware detection for IoT * End-to-end system security models for IoT * Machine Learning for security and privacy in IoT * Deep Learning for security in IoT * Machine learning for deep packet inspection for IoT * Machine learning to analyze cryptographic protocols for IoT * Privacy-preserving, machine-learning-based data analytics in IoT * Privacy enhancing and anonymization techniques in IoT IMPORTANT DATES Papers submission: March 15, 2020 Notification of acceptance: April 27, 2020 (Hard Deadline) Camera-ready paper due: May 11, 2020 (Hard Deadline) Workshop date: August 6, 2020 Further information on submission can be found at: https://sites.google.com/uw.edu/iotspt-ml-2020 Workshop Co-Chairs Geethapriya Thamilarasu, University of Washington Bothell Abhishek Parakh, University of Nebraska at Omaha