Special Issue on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2023) Submission Date: 2024-10-30 The "Internet of Things" heralds the connections of a nearly countless number of devices to the internet thus promising accessibility, boundless scalability, amplified productivity and a surplus of additional paybacks. The hype surrounding the IoT and its applications is already forcing companies to quickly upgrade their current processes, tools, and technology to accommodate massive data volumes and take advantage of insights. Since there is a vast amount of data generated by the IoT, a well-analysed data is extremely valuable. However, the large-scale deployment of IoT will bring new challenges and IoT security is one of them. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Continuously evolving models produce increasingly positive results, reducing the need for human interaction. These evolved models can be used to automatically produce reliable and repeatable decisions. Today's machine learning algorithms comb through data sets that no human could feasibly get through in a year or even a lifetime's worth of work. As the IoT continues to grow, more algorithms will be needed to keep up with the rising sums of data that accompany this growth. One of the main challenges of the IoT security is the integration with communication, computing, control, and physical environment parameters to analyse, detect and defend cyber-attacks in the distributed IoT systems.

The 4th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2023) is an international conference dedicated to promoting novel theoretical and applied research advances in the interdisciplinary agenda of Internet of things. This special issue includes selected papers (with no less than 60% new content of the journal version) from SPIoT 2023, Oct. 20-21, 2023, Fuyang, Anhui, as well as an open call.

Topics of interests include, but are not limited to:

Novel machine learning and big data analytics methods for IoT security

Big data analytics/machine learning/deep learning for IoT security such as smart grid security analytics

Data mining and statistical modelling for the secure IoT

Machine learning and big data analytics architectures for IoT security

Machine learning based security detecting protocols

Machine learning experiments, test-beds and prototyping systems for IoT security

Analytics and machine learning applications to IoT security

Data based metrics and risk assessment approaches for IoT

Data confidentiality and privacy in IoT

Authentication and access control for data usage in IoT

Data-driven co-design of communication, computing and control for IoT security

Big data analytics/machine learning/deep learning edge/fog security

Emerging standards for IoT security

Guest Editor

Jinghua Zhao, University of Shanghai for Science and Technology, China, zhaojinghua@usst.edu.cn

Important Dates

Manuscript Due: 30th October 2024

First Round of Reviews: 15th December 2024

Final Decision: 30th January 2025