Phd Studentship: Agile Defense: Leveraging Continual Learning For Adaptive Cybersecurity

Supervisory Team: Dr Erisa Karafili, Prof. Adam Sobey Project description: Cyber-attack techniques are becoming increasingly sophisticated, breaching even the toughest defenses and requiring smarter, more agile solutions. Current machine learning (ML) algorithms identify and predict threats but rely heavily on past datasets, requiring significant updates. Continual learning offers a solution by enabling automatic adaptation to new threats. In this PhD project, you will: -           Explore Continual Learning techniques to improve cyber-attack identification; -           Investigate the application of Continual Learning and Natural Language Processing (NLP) to automate the cyber-attack attribution process. -           Establish solutions for automatic identification of attacker-oriented countermeasures. You will explore how attackers adapt and evolve their techniques to overcome existing defenses and how Continual Learning can help in detecting these evolving threats. Additionally, you will analyze the provenance of used malware/attack techniques and the context of attacks to enhance the attribution process. This includes leveraging NLP techniques to parse and analyze threat intelligence reports, which will help in identifying the attackers and understanding their motivations and methods. Your work will involve a blend of technical and analytical skills, including malware analysis, intrusion detection system (IDS) features, and understanding the broader ecosystem of cyber threats. The goal is to create solutions that are not only technically robust but also contextually aware, leading to more effective and tailored cybersecurity measures. You will join the Cyber Security Research Group recognised as Academic Centre of Excellence for Cyber Security Research (ACE-CSR) and Education (ACE-CSE), and work with Dr Erisa Karafili and Prof. Adam Sobey. Funding for this project is offered by the Centre for Doctoral Training in Complex Integrated Systems for Defence & Security (CISDnS), which will recruit motivated and inquisitive candidates across the themes of Digital, Physical and Biological systems to provide a diverse and interconnected cohort training environment. As well as carrying out research training in a world-leading research group, membership of CISDnS will provide the opportunity for you to be exposed and trained to handle the interdisciplinary challenges faced in the real-world via a Systems Thinking approach. You will learn about the wider challenges of research and innovation within the Defence & Security sector. Entry Requirements This PhD studentship requires UK Citizenship. First Honours or 2:1 bachelors or masters degree in Computer Science, Mathematics, Engineering or related areas. Experience and/or a high interest in cyber security, AI and ML is a strong requirement. We welcome applicants onto the CDT from underrepresented groups. Closing date: 31st July 2024.  Funding: Full-time studentships will cover UK course fees and an enhanced tax-free stipend of approx. £23,500 per year for 4 years along with a substantial budget for research, travel, and centre activities. A number of studentships are available and will be awarded on a rolling basis, so you are encouraged to apply early for the best opportunity to be considered. How To Apply Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2024/25, Faculty of Physical Sciences and Engineering, next page select “Integrated PhD in Complex Integrated Systems for Defence and Security (2024-25)” In Section 2 of the application form you should insert the name of the supervisor Dr Erisa Karafili. Applications should include: Curriculum Vitae Two reference letters Degree Transcripts/Certificates to date