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),
in Honolulu, Hawaii, USA. 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We plan to seek previously unpublished work in theoretical or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


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:

Workshop Co-Chairs

Geethapriya Thamilarasu, University of Washington Bothell
Abhishek Parakh, University of Nebraska at Omaha