Context: Advanced cyber-attacks can massively disrupt the physical systems such electric vehicle (EV) infrastructure. It is not always possible to protect the system against potential threats and the available detection capabilities may not be enough. Therefore, it is vital to understand that the system is under attack, its potential impact and react with appropriate cyber incident responses. Intelligent edge devices embedded with AI/machine learning can help preparing the systems against cyber-attack responses with situational awareness. This work will consider possible EV charging bit configurations including sent to and from the controller, and the patterns between these input/output bit configurations. Then apply both, supervised and unsupervised machine learning algorithms to detect malicious data instances and attack patterns, and prepare businesses with effective and adaptive responses (analytic monitoring, separation, and dynamic representation) against cyber-attacks.
The School of Computer Science & Informatics has a strong emphasis on cyber security research due to recent grants, and also hosts the Airbus Centre of Excellence for Cyber Security and NCSC approved Academic Centres of Excellence in Cyber Security Research (ACE-CSR). Students attend research workshops and conferences, skills training through the Doctoral Academy, and have an opportunity to work with industry. A healthy research environment promotes research ideas and collaborations, and opportunities for networking through interdisciplinary work with the School of Engineering (Energy/EV research group). The students will be part of the Sustainable Transport Interdisciplinary Doctoral Training Hub (https://idth-sustainable-transport.org/) benefiting from training and activities, and can also interact with DTE Network+ (https://dte.network/).
Objectives: The objectives of this work are: (1) Identify potential cyber threats and risks; (2) Integrate situational awareness and forensics elements to incident-response techniques to develop a simulation tool/technique; (3) Developing a response model using AI/ML for mitigating identified risks.
- Surveys on risk, impact and state of the art: cyber incident-response of electric vehicle infrastructure
- Identifying advanced cyber incidents and indicators of compromise
- Assess cyber risks and evaluate effective incident-response techniques
- Develop response model applying AI/ML techniques to mitigate identified risks.
- Academic technical publications
Dr Neetesh Saxena
Prof Liana Cipcigan
Prof Omer Rana
A 2:1 or above Honours undergraduate degree or a master’s degree, in computing or a related subject. Applicants for whom English is not their first language must demonstrate their 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
- 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)
Please contact Dr. Neetesh Saxena to discuss this project.
If you have any questions or need more information, please contact COMSC-PGR@cardiff.ac.uk