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Explainable artificial intelligence for securing Internet of Things

   Cardiff School of Computer Science & Informatics

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  Dr Yuhua Li  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Internet of Things (IoT) consists of things, services, and networks, it connects interrelated smart devices, objects, animals or people to transfer data over a network to serve people better. The amount of data transferred with IoT systems is continuous, heterogenous and huge, which make IoT systems vulnerable than traditional network to malicious activities from attackers, so security and privacy of this highly automated network is a key challenge for the deployment of Internet of Things (IoT). It is constantly subject to adversarial attacks including denial of service, jamming, spoofing, eavesdropping, malware and privacy leakage. The limited resources (computation, battery, and memory) on IoT devices and the amount of data generated and communicated severely constrains the applicability of existing security measures to IoT systems. Even if a security system is effective at the time of deployment, it is prone to fail soon as attackers adapt more smarter strategies to foil the system and to avoid detection. Machine learning is a major tool for detecting adversarial attacks, human level explainability of detection results remain an open research in the security of IoT.

 This project aims to address these key challenges to secure future IoT systems with creative machine learning methods:

•         Investigating data streaming classification methods for effectively detecting known types of attacks and their variants in the future;

•         Developing computationally cheaper machine learning algorithms as well as robustness against eavesdropping attacks;

•         Optimising the offloading policy in dynamic radio environments to optimally distribute computational load over cloud, device and edge;

•         Investigating adversarial machine learning techniques to tackle attackers’ changing strategies.

•         Interpretating prediction results to support human to take trustworthy actions.

 Keywords: explainable machine learning, deep learning, edge computing, Internet of Things.

Contact Yuhua Li ([Email Address Removed]) for information on the project.

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.

Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.

How to apply:

Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below

Please submit your application before the application deadline 29th April 2022 via Computer Science and Informatics - Study - Cardiff University

In order to be considered candidates must submit the following information: 

  • Supporting statement 
  • CV 
  • In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD
  • In the funding field of your application, insert “I am applying for 2022 PhD Scholarship in Computer Science and Informatics”, and specify the project title and supervisors of this project in the text box provided.
  • Qualification certificates and Transcripts
  • References x 2 
  • Proof of English language (if applicable)

Interview - If the application meets the entrance requirements, you will be invited to an interview

If you have any questions or need more information, please contact [Email Address Removed]

Funding Notes

A School-Funded PhD Scholarship is available for entry 2022/23.
In the Funding field of your application, insert "I am applying for 2022 PhD Scholarship" and specify the project title and supervisor of this project in the fields provided.
This project is also open to Self-Funded students worldwide. If you are interested in applying for a Self-Funded PhD, please search FindAPhD for this specific project title, supervisor or School within its Scholarships category.


W Samek et al. (2021) “Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications,” Proceedings of the IEEE.
L Xiao et al. (2019) “IoT Security Techniques Based on Machine Learning,” IEEE Signal Processing Magazine.

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