Explainable artificial intelligence for securing Internet of Things [Self-Funded Students only]

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.

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

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

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

This project is accepting applications all year round, for self-funded candidates via https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics 

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
• Qualification certificates and Transcripts
• Proof of Funding. For example, a letter of intent from your sponsor or confirmation of self-funded status (In the funding field of your application, insert Self-Funded)
• 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 additional questions or need more information, please contact [Email Address Removed]