Title: Special Issue of Elsevier Journal, Internet of Things on Machine Learning for Security, Privacy and Trust in IoT Webpage: https://www.journals.elsevier.com/internet-of-things/call-for-papers/machine-learning-for-security-privacy-and-trust-in-iot Guest Editors: Abhishek Parakh and Parvathi Chundi, University of Nebraska at Omaha Aim and Scope: This special issue 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 platform. Topics * Machine learning based security, privacy, and trust issues in IoT * Security and privacy frameworks for IoT at home * Threat and attack model generation based on machine learning for IoT * Machine learning based intrusion and malware detection for IoT * System and data integrity * End-to-end system security models for IoT * Cryptographic approaches for security and privacy in IoT * Architectures and protocols for scalable, secure, robust and privacy enhancing IoT * Deep Learning for IoT * Machine learning for deep packet inspection for IoT * Machine learning to analyze cryptographic protocols for IoT * Novel machine learning and data science methods for IoT security * Data mining and statistical modeling for the secure IoT * Adversarial machine learning for IoT * Data based metrics and risk assessment approaches for IoT * Machine learning based authentication and access control in IoT * Fog security issues Important Dates Manuscript submission due: April 15th, 2019 1st Review Notification to authors due: July 15th, 2019 Revised Manuscript due: Sep 15th, 2019 2nd Review Notification to authors due: Dec 15th, 2019 Final notification due: Jan 15th, 2019 Notes for Prospective Authors See Guide for Authors http://www.elsevier.com/journals/internet-of-things/2542-6605/guide-for-authors Submission All papers must be submitted online. Submit your paper: https://www.evise.com/evise/jrnl/IOT