Netizens’ Security Perspectives

The goal of this project is to develop an effective in-silico model for dynamic social networks, aimed at simulating interactions and dependencies within social groups and individuals. This model will help identify role-risk relationships and highlight key individuals and communities, particularly in the context of Netizens' security.

Award

MRes | MPhil | PhD | MSc by Research

Start Date

Usually February and October - at individual School's discretion

Research Opportunities

Summary

Security Aspects of Netizens explores the intersection between human behaviour in digital spaces and cybersecurity practices. Netizens, or citizens of the internet, engage in online interactions that can expose them to cyber threats. This concept investigates how cultural, social, and psychological factors influence online security practices, such as password usage, privacy concerns, and susceptibility to phishing or other attacks. Understanding these patterns helps cybersecurity experts design more effective defences and awareness programs tailored to diverse online communities, addressing both technical and human vulnerabilities in the digital realm. It highlights the importance of balancing user freedom with security protocols.

Aims

Develop a Security-Focused Social Network Growth Model: Create a model to analyse interactions, dependencies, and information flow within social groups and individual users, specifically examining how these dynamics impact cybersecurity. By incorporating contextual data and observing dynamic user behaviour, the model will identify key communities and influential individuals, highlighting potential cyber vulnerabilities and security-critical interactions.

Establish an Attractiveness Matrix for Misinformation and Disinformation: Using insights from the network growth model, develop a security-specific matrix to evaluate the spread and influence of misinformation and disinformation within digital communities. This matrix will assess the susceptibility of different groups to such threats, providing a framework to predict and mitigate potential security risks.
Methods: Enhance existing social network growth models, such as Preferential Attachment, by integrating contextual and temporal data with a focus on cybersecurity. This approach aims to capture the evolution of social networks over time, highlighting link dynamics and security-relevant patterns, such as the spread of cyber threats or the identification of high-risk user clusters.

Expected Candidate

The ideal candidate should have strong research skills, with experience and expertise in Machine Learning, Social Network Analysis (preferably Graph Analysis), and Software Engineering.