Statistics, python, data analysis
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 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 goal of this project is to identify efficient ways to clean and analyse an existing set of raw data collected from dark web forum discussions. Furthermore, considering the real-world nature of the approach, the project can serve as a first step for providing insights on specific aspects of cybercrime and how criminal behaviours are formed in the dark web.
Who is eligible?
Ideally, the student should have some understanding of mathematical and statistical analysis, knowledge of python is essential, 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 for data cleaning and analysis.
Note: it is most important that you – as an applicant – are 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.