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The literature indicates significant advancements in securing IoT networks, especially against quantum computing threats. Bagchi 2023 proposes a post-quantum lattice-based secure framework called LAS-AIBIoT for ambient intelligence-assisted blockchain-based IoT applications. It utilizes aggregate signatures and demonstrates robustness against potential attacks, including quantum computing security threats. Biswas 2023 introduces a scalable meta-learning-based model for securing IoT networks. The model combines edge models and metadata-based learning to detect various attacks in IoT devices, improving accuracy and performance. Building on the ideas from Bagchi and Biswas about making IoT networks safer, this project mix Quantum Metamaterials, Artificial Intelligence (AI), and Large Language Models(LLMs) to create stronger security for IoT networks.
At the heart of this project is the creation and tailoring of Quantum Metamaterials to improve IoT security. These metamaterials will form the basis of a new physical security layer, enabling hidden communication, better intrusion detection, and secure data transfer within IoT networks.
Generative AI will be crucial in automatically designing and refining these quantum metamaterials. Using Generative Adversarial Networks (GANs) or similar algorithms, the project seeks to find new metamaterial structures with quantum features that enhance security. The generative models will continuously create and assess various metamaterial designs, speeding up the search for the best configurations to improve IoT security.
At the same time, Large Language Models (LLMs) will be used to create a smart security analysis framework. This framework will constantly examine the many interactions within the IoT network and the broader digital environment to spot potential security threats. The LLMs, trained on a wide range of cybersecurity texts and real-world intrusion data, will be able to identify complex cyber-attack patterns and suggest immediate solutions.
Moreover, a cooperative feedback loop between the LLM-driven analysis and the Generative AI-driven metamaterial design will be formed. Insights from LLM analysis will guide the generative algorithms, helping to continuously improve the quantum metamaterials to tackle emerging security threats. On the other hand, the actual performance data of the quantum metamaterials will be used to enhance the LLMs' understanding and prediction accuracy regarding cybersecurity challenges in IoT.
By blending quantum physics, artificial intelligence, and cybersecurity the goal is to develop a resilient, adaptable, and smart IoT security infrastructure that can foresee and counter a wide range of cyber threats in an increasingly interconnected digital world.
Academic qualifications
A first-class honours degree, or a distinction at master level, or equivalent achievements in Cybersecurity, Electrical and Electronic Engineering, Computer Science, Artificial Intelligence or Machine Learning.
English language requirement
If your first language is not English, comply with the University requirements for research degree programmes in terms of English language.
Application process
Prospective applicants are encouraged to contact the supervisor, Dr. Luigi La Spada ([Email Address Removed]) to discuss the content of the project and the fit with their qualifications and skills before preparing an application.
The application must include:
Research project outline of 2 pages (list of references excluded). The outline may provide details about
The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
Applications can be submitted here.
Download a copy of the project details here.
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