Guest Editors
Dr. Muhammad Arif, The University of Lahore, Pakistan.
Prof. Dr. Oana Geman, Universitatea Stefan cel Mare din Suceava, Romania.
Prof. Dr. Aniello Castiglione, University of Naples Parthenope, Italy.
Dr. Khurram Ejaz, University of Lahore, Pakistan.
Summary
This Special Issue aims to provide
state-of-the-art systems for malicious traffic identification in the Internet
of things (IoT) network using machine learning (ML) techniques. We, therefore,
wish to look complete systems for IoT malicious and management systems for the
IoT network traffic classification, which contributes to truly innovative
pervasive for the unwanted IoT traffic in IoT network environment. The goal of
the present Special Issue is to collect contributions in the disciplines of IoT
devices, computer science, engineering, machine learning, protocols, feature
selection and traffic classification, identification in IoT that extend the
current state of the art with innovative ideas and solutions. Experimental and
theoretical studies for IoT networks are encouraged.
Keywords
Internet of Things Network Traffic Classification
Intelligent systems for security and privacy in IoT Network
IoT Network Traffic Management Using Machine Learning
Malicious IoT Traffic Identification Using Machine Learning
QoE/QoS for IoT Network Management
Machine Learning Algorithms for IoT Traffic Classification
Technique for IoT Devices management
Authentication Technique for IoT Based on Machine Learning
Real-time online IoT traffic classification system based on Machine Learning
IoT Network Security Bases on Machine Learning
Other Topics
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