The 11th International Workshop on Security, Privacy, Trust, and
Machine Learning for Internet of Things (IoTSPT-ML 2021) will be held
in conjunction with the The 30th International Conference on Computer
Communications and Networks (ICCCN 2021)<http://www.icccn.org/>, in
Athens, Greece. All papers presented in IoTSPT-ML 2021 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


Internet of Things (IoT) has emerged as the next big technological
revolution in computing in recent years with the potential to
transform every sphere of human life. With an expanding network of
interconnected Internet-enabled devices, IoT devices are used in a
range of applications from connected cars, smart homes, healthcare,
smart retail, to supply-chain management. The rise of this
transformative technology is however deeply mired with security and
privacy concerns.

The large influx of connected devices in the market introduces new
vulnerabilities and opens new avenues for security attacks. The
massive scale and variety of these devices also make it challenging
for the manufacturers to design and implement manageable security and
privacy solutions resulting in devices shipped without adequate
security controls in place. Traditional security, privacy and
trust-based solutions are also found to be inefficient against the
various constraints of the IoT environment.

In the IoT ecosystem, where devices are constantly generating
increased volume of big of data, machine-learning algorithms can be
useful to perform intelligent processing, automated data analysis and
provide meaningful interpretations and predictions to support smart
and secure IoT applications. Machine learning techniques that enable
the IoT devices to learn and adapt to various threats dynamically will
be critical to building secure IoT systems. Use of machine learning
for IoT security is especially very promising to detect any outliers
to normal activity in the system.

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
 *   Machine learning for anomaly detection in IoT
 *   Machine learning to analyze cryptographic protocols for IoT
 *   Deep Learning for security in IoT
 *   Privacy-preserving Edge Machine Learning in IoT
 *   Privacy enhancing and anonymization techniques in IoT
 *   Attack against Federated Learning in IoT Systems

IMPORTANT DATES

Papers submission:                     March 5, 2021
Notification of acceptance:        April 23, 2021
Camera-ready paper due:           April 30, 2021
Workshop date:                           July 22, 2021


Further details on the workshop and submission process can be found at:

https://sites.google.com/uw.edu/iotspt-ml2021

Workshop Co-Chairs

Geethapriya Thamilarasu, University of Washington Bothell (geetha@uw.edu)
Abhishek Parakh, University of Nebraska at Omaha


Dr. Geetha Thamilarasu
Assistant Professor
Computing and Software Systems
University of Washington Bothell
http://faculty.washington.edu/~geetha