Security protocol analysis, modelling and testing protocols using process algebras and symbolic logic, enhancing automated theorem provers, quantum cryptography, low power cryptography, abstract modelling of network management architectures.

IPv6 security issues, issues surrounding IPv6 adoption and assimilation and the use of IPv6 in internet of things (IoT) applications

The forensic capture and analysis of network traffic, log files and data aggregated from multiple sources using data mining techniques.

Security and privacy in systems that aggregate data, particularly recommender systems. For example, eccentric users (users with unusual ratings, preferences as well as purchase and search history) are at a higher risk compared to average users, as they cannot hide in crowds of other users. There is therefore an inherent need to investigate the trade-off between utility and privacy in recommendation algorithms.

Processing in-network traffic of Internet of Things devices to improve efficiency and security. This includes the encoding data to increase security or transmission efficiency, the security of wireless communication (particularly in sensor networks), security issues resulting from networks of abundant embedded systems (IoT) and the construction compromised data-streams from sensing devices.

The trade-off between encryption strength and energy consumption. Stochastic and statistical methods can be used to examine this balance with an aim of developing a framework which can be used to adjust dynamically the level of security or energy consumption, either according to the energy limitations or the severity of the requested service.

Security issues and opportunities in software defined networking, particularly around new ways to model network interaction and enhancing network monitoring; and developing the ability for SDNs to reconfigure in order to protect the network infrastructure and devices.

Methods of generating regular expressions suitable for evolving forensic requirements that allow thorough examination of evidential data.

Mobile Devices Security. While existing Intrusion Detection Systems (IDS) are relatively effective in protecting main frame machines, servers and desktops, they are relatively inadequate when deployed on mobile devices which run in different and changing environments, requiring a fundamentally different approach in security analytics.

Intelligent Intrusion Detection Systems (iIDS). Developing Deep Learning models for adaptive Intrusion Detection/Prevention Systems. iIDS will be capable of learning from normal user behaviour, successful and unsuccessful hacking attacks and security policy violations as well as be able to adapt to new user/hacker behaviour and partially also to new environments.

Open Source Intelligence (OSINT) in Incident Response and Forensic investigation: Applying Artificial Intelligence models in analysing social media and other Internet content in Incident response and forensics, with an emphasis on applying Machine learning models in analysing language and image data in order to identify and collect relevant factual information related to specific incident/location/objects/people.