The defence and security sector has long been an environment rich in data, resulting in many scientific advances being published over the last 30 years. These advances have filtered down into other applications, such as healthcare, consumer technology, and the energy sector.
To continue to be at the cutting edge of data science, the defence and security community – represented by the Ministry of Defence (Defence Science and Technology Laboratory and Joint Forces Command) and GCHQ – are collaborating with The Alan Turing Institute to deliver an ambitious programme of data science research. The programme has three goals:
- To deliver world-leading research
- To deliver impact in real world scenarios
- To develop the next generation of data science leaders
The programme is led from The Alan Turing Institute headquarters in the British Library.
The Turing defence and security programme will solve challenges in three areas: urban analytics, cyber-security and social data science.
How to prevent and respond to urban security threats
As the physical environment in which we operate becomes increasingly technologically sophisticated, so does the complexity of security threats.
The need to understand the urban environment, in both developed and developing economies, requires an ability to produce robust modelling procedures at the micro and macro scale, as well as an ability to assess infrastructure vulnerabilities.
How to integrate information systems to improve cyber security
Cyber security is a key use-case and testing ground for the development of next-generation data science platforms and algorithms.
It is also a domain that presents some of the hardest challenges in data science: high-speed processing and data summarisation; data fusion across many unsynchronised sources; data sharing restrictions and imputation of missing data; and of the need for fast-changing models in response to new types of cyber attack.
There is a need for improved cyber-security, integrating knowledge of computer networks and information technology, and human factors.
Social data science
How to use data science to understand complex social systems
Social data science brings together scientists from a range of disciplines – computational, political, economic, health, statistical and social – to study and apply data in interpersonal, group, and policy contexts.
It covers tools, techniques, and data solutions required for the study of complex social systems – in particular, the study of individuals and the observable data generated in such systems.
Theory and Methods
These three challenge areas will be supported by theoretical and methodological advancements from across the wider data science spectrum.
Improving intelligent data systems - The development of underpinning methodologies for deriving information and intelligence from noisy and differing data sets (free text, imagery, video, audio, network logs, etc), including under-specification of data, veracity of data, data corruption, missing data and duplication.
Building privacy and trust - Trusted data activity is fundamental to government agencies and to a functioning data economy. Intelligent data systems should be underpinned by flexible, secure, trusted, authorised, and auditable data flows.
Starting with the appointment of Dr Mark Briers as Programme Director and the identification of three core research projects, the programme’s activity has rapidly expanded during 2017.
Additional research projects in our remaining two key challenge areas will begin before the end of the year, and several interdisciplinary research themes have evolved that cut across the Turing’s strategic partners.
Our activities are driven by the programme’s goals of delivering world-leading research with real-world impact.
Case Study: Data Study Groups
The defence and security programme has taken part in several Turing Data Study Groups, week-long events which bring together academics from around the UK as well as internationally, to work on data science problems provided by industry partners.
Firstly in May 2017, with industry participants including Siemens, HSBC, Samsung, and Thomson Reuters. The two challenges from the defence and security programme were focussed around machine learning for location prediction, and methodologies for analysis of a cyber-attack. Read our blog piece from one of the researchers involved.
And secondly in December 2017, where Dstl led a challenge for researchers to use machine-learning to help improve code quality analysis tools. Read the blog by Dstl representative John and piece on the Government website to find out more.
A series of scientific reports generated by the groups will be published soon.
House of Lords Select Committee on Artificial Intelligence
Defence and Security Programme Director, Mark Briers, was invited to speak to the House of Lords Select Committee on AI on 27 November 2017, during the first panel of a session entitled ‘What are the dangers of artificial intelligence?’.
Questions were around the UK’s capability to protect against the impact of AI on cyber security, and whether the law is sufficient to prosecute those who misuse AI for criminal purposes. The other panel member was Professor Christopher Hankin, Director, Institute for Security Science and Technology, Imperial College London.
Short-term projects aim to demonstrate immediate, meaningful impact
An announcement was made that the programme’s long-term work has been bolstered by a number of shorter, strategically important projects supported by funding from GCHQ. Each up to six months in duration, these projects aim to demonstrate immediate, meaningful impact, and address the key challenges that frame the defence and security programme.
The projects are focusing on a diverse range of applications including understanding hacker communities, adversarial machine learning, encryption, modelling of civil conflict, topological data analysis, and utilising game theory in cyber security. The projects are expected to yield academic impact through publications, and real-world impact through software, which will be released for use and further development.
The Manufacturer, the UK’s premier industry publication for providing manufacturing news, articles, and insights, published an article about the announcement.
Mapping conflict data to explain, predict, and prevent violence
Work produced by Turing Fellow Dr Weisi Guo is aiming to understand the mechanics that cause conflict and identify multi-scale population areas that are at risk of conflict. The research utilises the latest developments in complex networks and spatial interaction theory to model the effect of multiplexed regional-global interactions.
Findings have shown that ‘crossroad’ towns and cities where there are few other routes correlate strongly with data on violence; including terrorism, war between states, and gang violence. The work is building an evidence base which aims to help sustainable global development of infrastructure in order to reduce conflict.
Dr Guo was interviewed by BBC News about his work, the article going into depth about the potential ramifications of the work and the role of the Turing in the research.
For more information, please contact Catherine Lawrence, Programme Manager