Cybercrime and ransomware groups – data analysis (available)

Starting Date: March 2023
Prerequisites: Statistics, python, data analysis, ML
Will results be assigned to University: No

Project background

Cybersecurity is the practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks, in an ever increasingly complex threat landscape. These attacks constitute a variety of computer-enabled and computer-dependent crimes, broadly categorised as ‘cybercrime’. In order to be in a position to defend against these attacks and minimise the occurence of these crimes we need to understand how criminal behaviours and operational methods are formed.

A lot of cyber criminal activity takes place or at least originates on the so-called dark web. Users on the dark web enjoy anonymity, and this fact is taken advantage of, for conducting illegal activities online or coordinating for other more ‘traditional’ crimes.

The successful candidate will have the opportunity to join a team working on an ongoing project on cybercrime with the potential to publish their work.

Goal

The goal of this project is to explore criminal activities and communications within a number of datasets to gain insights on ransomware group and other criminal operations.

Who is eligible?

The successful candidate would have at least some understanding of statistical analysis, python and ML; however,specific knowledge of specialised software is not vital. Furthermore, the student should have willingness to study some relevant literature on behaviour and cyber security and willingness to learn methodologies, including quantitative and qualitative methods. for data cleaning and analysis.

Note: it is most important that you Рas an applicant Рare eager to learn new  theories, tools or methods needed, than having this specialised knowledge beforehand. That is, you are not expected to know specific tools for the analysis, but it is important to have a curiosity and eagerness to learn.

Note2: starting date to be agreed depending on the candidate’s availability.