Defeating complex families of malware using evolutionary based adversarial learning.

Malicious attacks account for a significant portion of attacks to information assets and computer networks in organisations today. More specifically, dangerous groups of malware that transform their code structures between generations such as metamorphic malware, provide a greater attack surface for the perpetuation of cybercrimes. This group of malware evade detection by conventional Machine Learning models using a number of code obfuscation strategies thus making them hard to detect. 

The proposed research will involves the use of evolutionary based adversarial learning approaches in defeating complex and dangerous malicious groups such as polymorphic and metamorphic malware. This involves the use of adversarial learning strategies in the generation of malicious mutants and the augmentation of training data with the produced mutants to improve the classification of such families of malware. 

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements ideally in Computer Science, Cyber Security or Artificial Intelligence.

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 Kehinde Babaagba (k.babaagba@napier.ac.uk) to discuss the content of the project and the fit with their qualifications and skills before preparing an application. 

Contact details

Should you need more information, please email SCEBERDL@napier.ac.uk.

The application must include: 

Research project outline of 2 pages (list of references excluded). The outline may provide details about

• Background and 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
• 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.
• Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
• Supporting documents will have to be submitted by successful candidates.
• Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.

Applications can be submitted here. To be considered, the application must use:

• the advertised title as project title

Download a copy of the project details here