Defeating complex families of malware using evolutionary based adversarial learning
About the Project
Malicious software continues to evolve rapidly, posing significant risks to information systems and organisational networks. Among these, polymorphic and metamorphic malware are especially dangerous, as they continuously alter their internal code structures through obfuscation and transformation, effectively evading traditional and machine learning-based detection methods.
This PhD project aims to develop evolutionary adversarial learning techniques to improve the detection of such adaptive and evasive malware. The research will explore how evolutionary algorithms can generate adversarial malware variants that simulate real-world transformations, and how these can be used to augment training data and enhance the robustness of machine learning classifiers. By modelling the dynamic interplay between attackers and defenders, the project seeks to advance next-generation, adaptive malware detection systems capable of responding to evolving threats.
The work will contribute to the fields of AI-driven cybersecurity and adversarial machine learning, with the long-term goal of improving resilience within automated malware analysis pipelines.
Objectives:
- Develop an evolutionary framework for generating realistic polymorphic and metamorphic malware variants.
- Apply adversarial learning to train robust classifiers capable of detecting previously unseen malware.
- Evaluate and benchmark model performance using real-world and synthetic malware datasets.
- Incorporate explainable AI to interpret detection outcomes and support human analyst understanding.
- Investigate the integration of the developed models into automated malware detection workflows.
Academic qualifications
A first-class honours degree, or a distinction at master level, or equivalent achievements in Computer Science, Cyber Security or Artificial Intelligence
English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.
Essential attributes:
Experience of fundamental software engineering and cybersecurity
- Competent in one or more programming languages
- Knowledge of Machine Learning and interested in Malware Detection techniques
- Good written and oral communication skills
- Strong motivation, with evidence of independent research skills relevant to the project
- Good time management
Desirable attributes:
Knowledge of Evolutionary Computing
APPLICATION CHECKLIST
- Completed application form
- CV
- 2 academic references, using the Postgraduate Educational Reference Form (download)
- Research project outline of 2 pages (list of references excluded). The outline may provide details about
- Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
- Research questions or objectives.
- Methodology: types of data to be used, approach to data collection, and data analysis methods.
- List of references.
- The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
- Statement no longer than 1 page describing your motivations and fit with the project.
- Evidence of proficiency in English (if appropriate)
To be considered, the application must use
- the advertised title as project title
For informal enquiries about this PhD project, please contact K.Babaagba@napier.ac.uk
Application Enquiries: https://www.napier.ac.uk/research-and-innovation/doctoral-college/application-guidance
Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?ElOlarlItFiG37xnH5PRRBvv3d563wLdwX4JfhYskMa3bJWTuc
Unlock this job opportunity
View more options below
View full job details
See the complete job description, requirements, and application process
