SPIoT2026 is an international conference dedicated to promoting novel theoretical and applied research advances in the interdisciplinary agenda of Internet of things. The 7th International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT 2026).

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome.

• 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 challenge 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 IoT security includes:

  (i) The information security of the cyber space.

  (ii) The device and environmental security of the physical space.

  These challenges call for novel approaches to consider the parameters and elements from both spaces, and get enough knowledge for ensuring the IoT's security. As the data has been collecting in the IoT, and the data analytics has been becoming mature, it is possible to conquer this challenge with novel machine learning or deep learning methods to analyse the data which synthesize the information from both spaces.

Committees

Program Committee

• Person 1

  John Macintyre The University of the Commonwealth Caribbean, Chancellor, Jamaica

• Person 2

  Jinghua Zhao University of Shanghai for Science and Technology, China

Organizing Committee

• Person 1 Jun Ye, Hainan University, China
• Person 2 Weidong Liu, Inner Mongolia University, China
• Person 3 Qingyuan Zhou, Changzhou Institute of Mechatronic Technology, Vice Chancellor, China

Contact

All questions about submissions should be emailed to spiot2026@126.com