Cyber Security, Artificial Intelligence, Machine Learning and Blockchain Technology: Mitigating Cyber Attacks and Detecting Malicious Activities in Network Traffic

About the project

About the Project Cyber attacks have become more widespread and several attacks have made headline news over the past decade, targeting industrial companies and governmental organisations. These attacks have caused substantial financial losses and were able to hinder the operation of core public services. Furthermore, since the Internet of Things (IoT) has emerged, the number of devices connected to the Internet is increasing rapidly and becoming easy targets for cyber attacks. To mitigate cyber-attacks, cybersecurity analysts heavily depend on Intrusion Detection Systems (IDSs) which can detect malicious activities by matching patterns of known attacks (i.e. signature-based) or observing anomaly activities (i.e. anomaly-based). Besides, blockchain technology provides high standards of data transparency and integrity. This project aims to tackle Trust, Identity, Privacy and Security (TIPS) issues in large-scale networks, Internet of Things, Wireless Communications and Smart Cities. The proposed research aims at developing a practically deployable cyber security solution to one or more of the cyber threats. Multi-Stage Attacks (MSAs), Advanced Persistent Threats (APTs), ransomware / malware, social engineering, Distributed Denial of Service (DDoS) attacks, wireless injection attacks, botnets or other malicious activities will be investigated. The developed methods aim to analyse the cybersecurity vulnerabilities and will be based on advanced Artificial Intelligence (AI) technologies. Network security monitor tools will be used to analyse the network traffic and a novel Intrusion Detection System (IDS) will be developed using cutting-edge Machine / Deep Learning algorithms. Moreover, the blockchain technology and smart contracts can be utilised to strengthen cybersecurity and keep information safe. The project offers the candidate new opportunities to gain invaluable experience in the relevant areas. The successful candidate will have the opportunity to work within a dynamic, effective and multi-disciplinary team. Person Specification Master's degree in Computer Science or related disciplines such as Information Security, Cyber Security, Computer Networks, Artificial Intelligence or Electronics and Electrical Engineering. Strong interest in Cyber Security, Artificial Intelligence, Machine Learning and Blockchain Technology. Programming experience (any language, e.g. Java, C, R, Python, Matlab) Good analytical skills - knowledge of foundations of computer science, ability to think independently Strong oral and written communication skills, in both plain English and academic languages, for publication in relevant journals and presentation at conferences How to apply Formal applications can be made via the University of Bradford web site ; applicants will need to register an account, select 'Postgraduate Research' as the course and then use the keywords 'computer science'. Applicants should 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.

Research areas