Enhancing Detection and Prevention of Malvertising in Web Browsers

Malvertising (short for malicious advertising) is a growing threat to individuals and organisations, as cybercriminals use it to distribute malware, steal sensitive data, and conduct phishing attacks. Recently, it has been observed that when individuals search for popular tools such as Notepad++, Zoom, AnyDesk, Foxit, Photoshop, and others on Google, they may encounter ads that redirect them to malicious sites or install malware on their computers or mobile devices, e.g., the attackers might use these viruses to track individual’s keystrokes, steal their passwords or take over their computer. Some malvertising is designed to trick individuals into giving up their personal information, especially financial information. As cybercriminals continuously refine their tactics, the threat of malvertising continues to grow, posing significant risks to personal privacy and organisational security.

Given its impact, there is a pressing need for robust detection and prevention mechanisms within web browsers. Research into malvertising detection techniques, such as enhanced browser security features, real-time threat intelligence, and machine learning-based anomaly detection, is crucial for mitigating this growing threat. This PhD research aims to investigate advanced techniques and develop a novel tool(s) to detect and prevent malvertising in web browsers, ultimately contributing to a stronger security posture for individuals and organisations.

The objectives of this research include:

• Conduct an in-depth review of existing research on malvertising, including the latest detection and prevention mechanisms integrated into web browsers.
• Identify and classify different forms of malvertising (e.g., drive-by downloads, crypto-jacking, social engineering attacks) and assess their potential impact on organisational systems, data security, and user privacy.
• Propose a novel detection and prevention mechanism to address malvertising in web browsers, focusing on scalability, real-time detection, and ease of implementation.
• Design and simulate malvertising attacks through penetration tests to evaluate the effectiveness of the proposed detection and prevention technique.
• Evaluate the proposed solution in terms of effectiveness, usability, system performance, and potential integration with existing web browsers.

The research project will take place at the Cyber Security Interdisciplinary Centre, which comprises a team of experts specialising in network security, AI-based solutions for cybersecurity, penetration testing, and intrusion detection systems. These knowledgeable professionals will offer valuable assistance for the student involved in the project. Requirements: The candidate must have experience in network security, malware analysis, threat detection, and conducting penetration tests.

How to apply

Formal applications can be submitted via the University of Bradford web site; applicants will need to register an account and select 'Full-time PhD in Computer Science' as the course, and then specify the project title in the 'Research Proposal' section.

About the University of Bradford

Bradford is a research-active University supporting the highest-quality research. We excel in applying our research to benefit our stakeholders by working with employers and organisations world-wide across the private, public, voluntary and community sectors and actively encourage and support our postgraduate researchers to engage in research and business development activities.

Positive Action Statement

At the University of Bradford our vision is a world of inclusion and equality of opportunity, where people want to, and can, make a difference. We place equality and diversity, inclusion, and a commitment to social mobility at the centre of our mission and ethos. In working to make a difference we are committed to addressing systemic inequality and disadvantages experienced by Black, Asian and Minority Ethnic staff and students.

Under sections 158-159 of the Equality Act 2010, positive action can be taken where protected group members are under-represented. At Bradford, our data show that people from Black, Asian, and Minority Ethnic groups who are UK nationals are significantly under-represented at the postgraduate researcher level. 

These are lawful measures designed to address systemic and structural issues which result in the under-representation of Black, Asian, and Minority Ethnic students in PGR studies.