Cyber Security Analytics with Big Data
Supervisory team: Dr Wei Jie
Start dates: January, May and September of each academic year
Duration: This is a three-year position.
Research at the University of West London lives in an ecosystem of interdisciplinary research. This PhD position is based in the School of Computing and Engineering.
According to research firm Gartner Big Data analytics will play a crucial role in Cyber Security. Cyber Security analytics with Big Data will let organisations sift through massive amounts of security-related data — generated inside and outside the organisation — to uncover hidden relationships, detect patterns and remove security threats. This will enable organisations to see a bigger and broader picture of the security landscape for their organisations. Cyber Security analytics with Big Data is applicable in many security use cases such as network monitoring, authentication and authorization of users, identity management, fraud detection, and systems of governance, risk and compliance. This new technology will change also the nature of such Cyber Security controls as conventional firewalls, anti-malware and data loss prevention.
The information needed to uncover security events loses value over time and timely intelligent data analytics is critical as cyber criminals move much more quickly to commit their attacks. Therefore Cyber Security analytics must blend real-time analytics on data in motion with historical analysis on data at rest. By deploying security-specific analytics, organisations can find new associations or uncover patterns and facts. This real-time insight can be invaluable for detecting new types of threats as well.
In this project, we will work on real-time cyber-attack prediction and mitigation solutions leveraging Big Data analytics, in order for organizations to detect new threats early and react quickly before they propagate. The School works closely with world-class industrial partners (e.g. SEGA Europe Ltd, Amazon UK) to drive this project with real-world enterprise security scenarios. More specifically, this project aims to:
- Design innovative algorithms and a model for real-time Cyber Security analytics to detect anomalies and abnormal behaviours immediately. Huge volumes of Big Data from diverse sources need to be observed, analysed, and visualised in real-time manner to achieve advanced predictive capabilities and automated controls.
- Develop a software tool that implements the proposed algorithms and model, in particular, based on open source large-scale Big Data processing platform (e.g. Apache Hadoop and its ecosystem).
- Demonstrate and evaluate the Cyber Security analytics tool on Amazon Cloud. Experiments will be conducted to benchmark the performance of the developed algorithms and model.
The ideal candidate should have an MSc or equivalent degree in Computer Science/Cyber Security and combine solid theoretical background and excellent software development skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data, and conclude with the validation of a proposed solution through real applications/case studies.
Background knowledge and/or previous experience in the following areas/technologies, will be considered very favourably:
- Cyber Security knowledge and skills
- Big data processing and analytics
- Cloud computing architecture, infrastructure, and solution design
All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.
For general enquiries about the application process visit the Graduate School pages.
Questions regarding academic aspects of the project should be directed to firstname.lastname@example.org.