Proposed Project Title: Mastering Network Security: Hands-On Lab Experience

Principal Supervisor: Arnab Kumar Biswas

Project Description: Are you ready to dive into the world of network security and become a cybersecurity expert? Join us for an exciting 2-month project where you'll get hands-on experience with cutting-edge tools and techniques used by professionals in the field. This project is designed to give you practical skills and knowledge that will set you apart in the cybersecurity industry.

Objectives:

Some major objectives are-

1.     Create Virtual Machines:

·       Set up Security Onion and Kali Linux VMs to create a robust network security environment.

·       Learn how to configure and manage virtual machines for optimal performance.

2.     Install and Configure Security Tools:

·       Get hands-on with powerful tools like Suricata, Zeek, Elasticsearch, Kibana, and more.

·       Install and configure these tools to monitor and analyse network traffic.

3.     Simulate Network Attacks:

·       Use Kali Linux and Metasploit to simulate real-world network attacks.

·       Test the detection and response capabilities of your security setup.

4.     Perform Threat Hunting and Analysis:

·       Conduct threat hunting exercises using ATT&CK Navigator and CyberChef.

·       Analyse logs and alerts to identify and mitigate potential threats.

Academic Requirements:

The scheme is open to EEECS Undergraduates in BSc/Beng/Meng Computer Science and Software Engineering. A minimum current average classification of 65% average required, higher average classification will be recommended and used as part of the ranking criteria.

General Information:

The following skills will benefit the project, but you can learn them during the project also.

Technical Skills:

1.     Basic Knowledge of Networking:

·       Understanding of network protocols (TCP/IP, HTTP, DNS).

·       Familiarity with network devices (routers, switches, firewalls).

2.     Linux Fundamentals:

·       Basic commands and navigation in a Linux environment.

·       Experience with installing and configuring software on Linux.

3.     Virtualization:

·       Understanding of virtual machines and how to create and manage them using VMware or VirtualBox.

4.     Basic Cybersecurity Concepts:

·       Knowledge of common cybersecurity threats (malware, phishing, DDoS attacks).

·       Understanding of intrusion detection and prevention systems.

Soft Skills:

1.     Problem-Solving:

·       Ability to troubleshoot and resolve technical issues.

·       Analytical thinking to understand and mitigate security threats.

2.     Documentation:

·       Ability to document processes, configurations, and findings clearly and concisely.

Proposed Project Title: Efficient DC power combining circuit for wireless electric vehicle charging

Principal Supervisor: Dr Neil Buchanan

Project Description:

Recently we have been researching microwave wireless power transfer that allows 100’s of watts of power to be conveyed over distances of several metres. We are currently exploring several application areas including trickle charging of electric vehicles (EVs) where the EV can be wirelessly charged without a cable and without accurate positioning of a wireless charger.

To receive the wireless power an array of rectifying antennas (known as rectennas) is used. Each of these rectennas will receive a slightly different power level in terms of current and voltage. We need a circuit that can combine the power from these rectennas in an efficient way, as they cannot simply be connected in series or parallel without being very inefficient. The internship will look at building prototype DC DC converter circuits to efficiently combine power from unequal sources.

Objectives:

·       Put together a practical demonstration of an efficient DC power combining system for at least four rectennas. The concept can also be proved by simulation. Equivalent circuits may be used for rectennas, instead of microwave sources.

·       The building block of the stable phase reference will be a DC DC converter, which will be designed based on known methods, allowing the experimentation to start fairly quickly at the start of the internship.

·       Some knowledge of practical electronics is required, knowledge of DC DC converters, power electronics and radio/microwave particularly welcomed.

·       The internship will provide an excellent opportunity for engagement with relevant industry via a live proof of concept project.

Academic Requirements:

The scheme is open to EEECS Undergraduates in BSc/Beng/Meng Computer Science and Software Engineering. A minimum current average classification of 65% average required, higher average classification will be recommended and used as part of the ranking criteria.

GENERAL INFORMATION

Location: Centre for Wireless Innovation (CWI) in QtQ building, Titanic Quarter, Belfast.

Contact detail

Supervisor Name: Dr Neil Buchanan

QUB Address:

QTQ

Queen's University Belfast

Northern Ireland Science Park

Queen’s Road

Queen’s Island

Belfast

BT3 9DT

Email: n.buchanan@qub.ac.uk

Proposed Project Title: Breaking the Safety Chains – Jailbreak attacks to manipulate Small Language Models

Principal Supervisor: Ihsen Alouani

Project Description:

This project aims to investigate the state-of-knowledge in language models, particularly Small Language Models, and their vulnerability and alignment compared to Large Language Models.

Objectives:

• To survey the state-of-the-art in terms of small language models and safety alignment
• To investigate existing jailbreak techniques applied to large language models (LLMs) and assess their applicability to small language models (SLMs).
• To implement various jailbreak techniques on selected SLMs and evaluate their effectiveness.
• To analyze the security implications of jailbreak methods and propose possible mitigations.

Background & Motivation: Language models, including small-scale versions, are increasingly used in various applications. While safety mechanisms are integrated to prevent misuse, adversarial jailbreak techniques have demonstrated vulnerabilities in larger models. Understanding how these techniques transfer to smaller models is critical for assessing risks and designing robust defenses.

Methodology:

• Literature Review: Research existing jailbreak methods, focusing on adversarial prompts, prompt leakage, fine-tuning attacks, and reinforcement-based adversarial attacks.
• Model Selection: Choose 2–3 small language models (e.g., GPT-2 small, Llama-2 7B, or Mistral 7B) as test cases.
• Implementation of Jailbreak Techniques: Implement and test jailbreak attacks, including:
• Prompt injection attacks (manipulating input to bypass restrictions)
• Self-reflection loops (inducing models to subvert their guardrails)
• Gradient-based adversarial inputs (if fine-tuning is accessible)
• Evaluation: Assess success rate, robustness, and impact of jailbreaks on different architectures.
• Mitigation Strategies: Explore existing safety mechanisms and propose possible countermeasures.

Expected Outcomes:

• A state-of-the-art on SLMs and alignment
• A comparative analysis of jailbreak vulnerabilities in SLMs.
• A documented implementation of jailbreak techniques and their effectiveness.

Objectives:

Academic Requirements:

The scheme is open to EEECS Undergraduates in BSc/Beng/Meng Computer Science and Software Engineering. A minimum current average classification of 65% average required, higher average classification will be recommended and used as part of the ranking criteria.

General Information:

Proposed Project Title:

PostQuantumCore: Quantum secure algorithms on the processor inside my phone

Principal Supervisor:

Dr. Ayesha Khalid

Project Description:

Most common embedded processors (microcontrollers) are simple, low-cost and low power devices, that come up with a simple instruction set. The ARM family of embedded processors is the most widely used RISC (reduced instruction set computer) architecture processors in use today, with over 200 billion ARM chips produced till 2021^[1]. They are extensively used in smartphones, laptops and other embedded systems; list of ARM vendors includes Sharp, Samsung, Philips, intel, Apple etc.

This internship opportunity will provide the student a hands-on experience to learn the use of these processors. The student will be provided with an ARM Cortex-M4 processor chip on a Nucleo board (nucleo-l4r5zi, see picture) to keep during the internship, the toolchain for these simple boards can be installed on your laptop and they come with a single USB connection for both programming and debugging. This internship would provide a valuable learning experience for the student, as they would learn to use the toolchain to program the board and implement simple cryptography algorithms.

[1] https://www.arm.com/blogs/blueprint/200bn-arm-chips

Objectives:

This project will give you confidence in working with new technologies and tools, enhancing your problem-solving skills and your understanding of security. You will learn traits that can lead to a potential career path in future, i.e., effective documentation and analysis of your findings related to the project, develop communication and presentation skills, learn how to work effectively within a professional team, etc. Some technical objectives are listed below.

1. To gain practical experience of working with ARM Cortex-M4 processor chip on a Nucleo board. You will learn the use of the toolchain to program simple algorithms on board. (https://www.st.com/en/evaluation-tools/stm32-nucleo-boards.html )
2. Learn and appreciate the importance of cybersecurity in modern engineering projects and learn basic cryptography algorithms along the way.
3. Learn to benchmark the latest post quantum secure cryptography algorithms (recently standardized by National Institute of Standards and Technology, NIST in the USA) to evaluate their suitability for IoT applications. Exploring optimizations that may positively affect impacts code size, memory usage, throughput performance of an algorithm.

Academic Requirements:

The scheme is open to EEECS Undergraduates in BSc/Beng/Meng Computer Science and Software Engineering. A minimum current average classification of 65% average required, higher average classification will be recommended and used as part of the ranking criteria.

General Information:

Proposed Project Title:

Privacy enhancing technologies in the Metaverse

Principal Supervisor:

Ciara Rafferty

Project Description:

This project focuses on exploring privacy-enhancing technologies (PETs) in the context of the Metaverse.  As virtual environments grow in popularity, the need to protect users’ personal data and privacy becomes increasingly vital.  Privacy enhancing technologies are a range of technological solutions that can support and increase user privacy.    They enable privacy preserving computations and/or data access, each with a variety of associated benefits and challenges surrounding implementation and usage in real world scenarios.  This project will analyse how privacy is maintained in immersive Metaverse environments, and explore the potential of implementing PETs to address privacy concerns in the Metaverse, whilst investigating scalability, practicality and security concerns associated with PETs technologies.   The output of this project will be a summary report indicating suitability of PETs for metaverse related applications.

Objectives:

1.     To become familiar with the state of the art in privacy enhancing technologies.

2.     To identify PETs use case(s) in the Metaverse.

3.     To implement one (or more) existing PETs solutions for chosen use case(s).

4.     Evaluate implementation for suitability in Metaverse applications in terms of practicality, scalability, and security.

5.     To produce a summary report, based on findings, on PETs in the Metaverse.

Academic Requirements:

The scheme is open to EEECS Undergraduates in BSc/Beng/Meng Computer Science and Software Engineering. A minimum current average classification of 65% average required, higher average classification will be recommended and used as part of the ranking criteria.

General Information:

This project would suit students interested in privacy and with some background in applied cryptography or cyber security. Familiarity with programming would be helpful.

Proposed Project Title:

VR Lab Intern

Principal Supervisor:

Dr Matthew Collins

Project Description:

Opportunity to work on a variety of projects with a range of VR and AR hardware. Gaining valuable insight into postgraduate education through exposure to MSc software development projects.

Primary goals will be building on the work of previous VR lab interns who have been working on VR and AR apps particularly in Mixed Reality areas producing prototype experiences for use by colleagues in Bio Sciences/Social Sciences, Education & Social Work among others.

Likely focus of this specific internship would be development of experiences suitable for use at open day events in the csb. Likely looking into mixed reality multi user experiences with shared spatial anchors.

This internship can fall under multi-multidisciplinary research on both a computing, software development, education domain and a number of other disciplines.

Objectives:
requirements analysis and exploration of problem domain(s) – e.g. examining an existing VR protype projects developed and proposing potential areas where newer technologies could enhance and provide additional opportunities

development of a prototype Mixed Reality app suitable for use in in CSB based open day and outreach activities

app would likely be developed for Meta Quest but exact platform is open (potential for more ambitious directions to be explored – Multi device support etc.)

A successful prototype developed at this stage could perhaps lay the ground work for a more ambitious final year project in the future

Academic Requirements:

The scheme is open to EEECS Undergraduates in BSc/Beng/Meng Computer Science and Software Engineering. A minimum current average classification of 65% average required, higher average classification will be recommended and used as part of the ranking criteria.

Strong marks in programming related subjects
Comfort with independent learning and experimentation.

Experience with Unity game engine development (or other game engines e.g. Unreal/Godot) in spare time would be a plus and make getting up and running quickly easier, but not a must have requirement.

General Information:

Student will work out of the VR lab in the CSB
Start date open to negotiation, though likely to run between June and July

Proposed Project Title:

Fast & Furious with self-drive AI

Principal Supervisor:

Dr. Yun Wu

QUB Address: Room 02.08, 16 Malone Road, Belfast, BT9 5BN

Email: yun.wu@qub.ac.uk

Project Description:

Simultaneous Localization and Mapping (SLAM) is a crucial technology that enables autonomy in robotics and unmanned vehicles. SLAM allows these systems to map their environment and determine their location within it simultaneously. However, implementing SLAM involves processing vast amounts of sensor data and making decisions using complex optimization algorithms, which demands significant computational resources. This poses a substantial challenge when deploying SLAM on micro-robotics or vehicles, particularly those with limited computational power and energy supply, such as battery-powered devices.

In this project, students will utilize the JetRacer AI platform to deploy SLAM for autonomous vehicle applications, integrating camera and LiDAR sensors. By applying their knowledge of SLAM, students will explore and evaluate the performance capabilities of SLAM and assess the computational workload required. This hands-on experience aims to deepen their understanding of SLAM principles, machine learning, deep learning, and embedded systems. Additionally, it will enhance their programming skills in C/C++ and Python, providing a comprehensive learning experience that bridges theoretical knowledge and practical application.

Objectives:

1. To be familiar with JetRacer AI platform, including software for sensor data processing, vehicle control, and robotic operation systems, etc..

2. To be familiar with SLAM using JetRacer, including simple machine/deep learning, object detection, and obstacle avoidance, etc..  

3. To learn vehicle driving model and apply to SLAM on JetRacer, including two wheel modelling, and Model Predictive Control (MPC), etc..

Academic Requirements:

The scheme is open to EEECS Undergraduates in BSc/Beng/Meng Computer Science and Software Engineering. A minimum current average classification of 65% average required, higher average classification will be recommended and used as part of the ranking criteria.

General Information:

Accommodation and travel costs are not provided under this scheme.

Expected start date:  9^th June -  8^th August

Duration: maximum 8 weeks – preferably from June to early August

Location: CSB project room or Ashby building meeting room

Proposed Project Title:

Challenging the record of solving Sokoban problems (a.k.a. Investigating single-agent and multi-goal Pathfinding algorithms)

Principal Supervisor:

Zheng Li

Project Description:

“Sokoban, literally referring to a warehouse keeper, was created by Hiroyuki Imabayashi and is a cult classic. This game is a transportation puzzle where the playing arena is composed of a grid of squares. Some of the squares are marked as crates where the player has to push to a storage location in the warehouse. Some of the squares are marked as walls which act as constraints where the player as well as the crates cannot enter.”

Although it looks fun (https://youtu.be/cHdUigO1_lc), solving Sokoban problems is a serious research topic. In fact, Sokoban is known to be NP-hard and PSPACE-complete, and it is not currently possible to determine with certainty whether an arbitrary board is solvable, nor what the most efficient solution to a known-solvable board is. There have been continuous research efforts on this topic, including the famous AI company DeepMind. For example, a recent study claims “Our RL agent can solve hard instances that are far out of reach for any previous state-of-the-art Sokoban solver. In particular, our approach can uncover plans that require hundreds of steps, while the best previous search methods would take many years of computing time to solve such instances.” It will be interesting at least to replicate their study to verify if this is true against the standard benchmark that is composed of 90 mazes (https://webdocs.cs.ualberta.ca/~games/Sokoban/status.html).

Some selected references include:

[1]   Festival Sokoban solver (https://festival-solver.site/)

[2]   Imagination-Augmented Agents for Deep Reinforcement Learning (https://papers.nips.cc/paper_files/paper/2017/file/9e82757e9a1c12cb710ad680db11f6f1-Paper.pdf);

[3]   A novel automated curriculum strategy to solve hard Sokoban planning instances (https://dl.acm.org/doi/abs/10.5555/3495724.3495988);

[4]      The Sokoban Challenge: An Analysis on Past, Present, and Trends in Algorithms and Heuristics for Automatic Solving of Sokoban Problems (http://sokoban.dk/wp-content/uploads/2016/02/documents.mx_the-sokoban-challenge.pdf).

Objectives:

This project has two research directions with different objectives for you to choose (based on your interests and capabilities). In brief, one direction will let you study broad, while another direction will let you study deep:

·       Replicating selected Sokoban solvers and comparing their performance.

·       Developing new algorithms or improving the current methods (e.g., by detecting more deadlock status and/or by developing new heuristics) to solve Sokoban problems.

Please be advised that a Sokoban visualisation testbed has already been available, and you only need to read the source code and get familiar with it (or adapt it to your own code).

Academic Requirements:

The scheme is open to EEECS Undergraduates in BSc/Beng/Meng Computer Science and Software Engineering. A minimum current average classification of 65% average required, higher average classification will be recommended and used as part of the ranking criteria.

General Information:

Proposed Project Title: XR for Automated Assembly of Intermeshed Steel Connections

Principal Supervisor:

Prof. Karen Rafferty, Dr. Daniel McPolin

Project Description:

We seek a motivated and skilled undergraduate or master’s student to join our cutting-edge research project as an intern. The internship will involve working on innovative computer vision and extended reality (XR) applications, contributing to a high-impact project to advance robotic assembly and perception systems. The intern will work closely with our research team, gaining hands-on experience with industry-relevant tools and techniques.

Our research focuses on developing XR-based solutions to understand ergonomics during manual assembly tasks involving Intermeshed Steel Connections (ISC). The project involves: 

·     Building XR tools to analyse and optimise human ergonomics during ISC assembly. 

·     Collecting datasets during interactive ISC assembly tasks using XR environments. 

·     Integrating depth cameras and XR technologies for real-time data collection and analysis. 

·     Enhancing user interaction within the XR environment to support seamless data collection and feedback during assembly processes. 

The intern will be an integral part of this project, assisting in creating Unity-based environments, implementing algorithms in Python, and exploring novel applications of XR in assembly ergonomics. 

Objectives:

·     Design and implement Unity-based simulations for dataset generation. 

·     Assist in programming and debugging scripts in C# and Python. 

·     Contribute to the development of XR-based tools and workflows. 

·     Collaborate on data annotation and preprocessing for machine learning tasks. 

·     Participate in regular team meetings and provide updates on progress. 

Academic Requirements:

The scheme is open to EEECS Undergraduates in BSc/Beng/Meng Computer Science and Software Engineering. A minimum current average classification of 65% average required, higher average classification will be recommended and used as part of the ranking criteria.

General Information:

Required Skills: 

·     Familiarity with building and deploying XR applications (e.g., AR/VR). 

·     Proficiency in Unity for XR and 3D environment development. 

·     Working knowledge of C# and Python programming. 

·     Basic understanding of computer vision concepts and machine learning frameworks. 

·     Strong problem-solving skills and ability to work independently. 

Preferred Skills (not mandatory): 

·     Experience with depth cameras (e.g., Intel RealSense) or other sensor technologies. 

·     Knowledge of object detection and pose estimation techniques. 

·     Background in robotics or robotic assembly tasks. 

Proposed Project Title:

Research in Intelligent Sustainable Energy Systems within the Cyber Physical Systems Lab

Principal Supervisor:

Dr David Laverty

Project Description:

This internship will take place in the Cyber Physical Systems Lab (CPSL) located in the Ashby Building. The intern will have the opportunity to contribute to research in intelligent sustainable energy systems. The project will involve exploring various aspects of modern energy systems, including digital substations, GNSS spoofing, cyber security, virtualisation of servers, and data analysis. The intern will work closely with researchers to develop, test, and evaluate innovative solutions aimed at enhancing the security, efficiency, and sustainability of energy systems.

The intern will have the opportunity to tailor their interests within the following topic areas.

Topic Areas:

·       Gain hands-on experience in working with digital substations and their components.

·       Investigate and analyse GNSS spoofing threats and mitigation techniques.

·       Explore cyber security challenges in critical energy infrastructure.

·       Implement and test virtualisation techniques for energy system servers.

·       Conduct data analysis to extract insights and improve system performance.

·       Develop technical and research skills through collaboration with experienced researchers in the CPSL.

·       Open Source software for sustainable energy monitoring.

Objectives:

·       Develop Research Methodology Skills

·       Enhance Technical Writing and Communication Skills

·       Gain Hands-on Experience with Advanced Technologies

·       Foster Independent and Critical Thinking

·       Collaborate with Experienced Researchers

·       Prepare for Postgraduate Study and Research Careers

Academic Requirements:

The scheme is open to EEECS Undergraduates in BSc/Beng/Meng Computer Science and Software Engineering. A minimum current average classification of 65% average required, higher average classification will be recommended and used as part of the ranking criteria.

General Information:

For further information, please contact Dr Laverty via the means below:

Supervisor Name:          Dr David Laverty                                              

QUB Address:                Ashby 08.021

Email:                           david.laverty@qub.ac.uk