UCL Jill Dando Institute of Security and Crime Science


Dawes Centre for Future Crime

Technological and societal change leads inevitably to new types of crime. The Dawes Centre identifies emerging crime threats and works to deliver pre-emptive interventions for the benefit of society

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About us

The Dawes Centre was founded with £7m funding from the Dawes Trust and UCL. The centre focuses on key questions such as "which emerging crimes should we focus on, given limited resources?" and "how can we mitigate future threats?". Details about our work can be found below.

Director's Welcome

Professor Shane Johnson, Director, Dawes Centre

“The past thirty years have demonstrated that paradigm shifts in technology lead inevitably to the emergence of new types of crime and offenders. We are all familiar with the rapid rise of cybercrime brought on by the ‘digital revolution’, but crime opportunities will also be presented by developments in, for example, nanotechnology, robotics and cybernetics.

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"The relationship between innovation and crime has almost universally comprised three distinct phases. First, there is innovation without much consideration of crime consequences; second there is a crime ‘harvest’ exploiting the vulnerabilities to crime of the innovation; third, there is the retrofit of a solution, often partial. This sequence has been played out many times, most recently in the rush to market of digital products and services with unforeseen crime consequences.

"These ‘emerging’ crimes range across many crime markets. The Dark Web’s Silk Road and Bitcoin were major players in the illegal drug market, and the Dark Web still serves markets from counterfeit passports to pornography. There is a massive issue in online grooming preparatory to child sexual exploitation. One-to-many digital communication is a boon for fraudsters who need only one vulnerable target for every hundred sent in a single keypress. New Scientist reports a claim that handcuff keys used by Dutch police were 3-D printed having featured in newspaper photographs. The Internet of Things (machine to machine communication) offers a new range of criminal possibilities, such as the disruption of implanted medical devices. Arduino, an open-source electronics platform, brings the potential for weaponising a range of hitherto benign products.

"In a very real sense such ‘crimes of the future’ are an emergent property of the advance of civilisation. It is not a question of if new criminal opportunities will be exploited, but when and how. The challenge is in both forecasting the nature and spread of such crimes, and in tackling them effectively before they become established.

"Responses must be speedy, flexible and intelligent. The aim is to shorten movement through the three-stage cycle described above, and ideally pre-empt the first two. A range of disciplines across the social, physical and life sciences is relevant to this cause.

"However, the experience of cybercrime has demonstrated amply that law enforcement – often limited by constrained resources, out-dated skills-sets and traditional modes of thinking – may be caught flat- footed by crimes that emerge dramatically and with little advance notice of their arrival. It is in response to this clear gap that academic research, linked to a mechanism by which such research can be deployed into front-line law enforcement, can help.

"The proposed Dawes Global Centre for Future Crimes will have the dual purposes of identifying emergent crime threats and outlining and promulgating pre-emptive measures. To our knowledge this will be the first research centre of its kind anywhere in the world, giving us the opportunity to set the agenda. We welcome your participation in this endeavour.”

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The aim of research conducted through The Dawes Centre for Future Crime is to anticipate how technological or social change might create new opportunities for offending, and to propose methods for addressing these problems before they become established. Research will focus on a mixture of new crimes about which little is known, and crimes that are likely to emerge in the near future, or medium- to long-term time horizons.  

Projects will comprise two phases:

Phase 1

The aim of Phase 1 projects is to review what is known about a particular change - be it social or technological.  They will establish the state of the art on a particular topic and the implications for future crime.  Phase 1 projects will vary in terms of their aims but as a minimum they will involve detailed scoping activities to enable us to better understand a particular potential problem and how it might be addressed.

These will include sandpit activities to bring together world leading academics, practitioners and others to discuss a particular problem and what might be done about it.  Seed funding is available for Phase 1 projects and key deliverables will include a briefing paper on the topic, potential solutions to future crime problems identified, and a detailed proposal outlining what Phase 2 of the research project would look like, if funded.

Phase 2

The aim of Phase 2 projects is to complete original research intended to address a specific future crime problem, or to develop existing research to reach a technology readiness level suitable for deployment by crime reduction agencies.

To support Phase 2 projects we have funding available for Dawes research fellows (6-12 months duration), PhD scholarships, and Dawes International Exchange funding.

Recent projects


The Dawes Centre funds a wide variety of PhD research in conjunction with UCL SECReT, the UCL Security Science Doctoral Training Centre, the international centre for PhD training in security and crime science. Our scholarships are available for ‘pre-set' topics or for ‘open topics'. Pre-set topics are specific topics that have been suggested by supervisors here at UCL and which they will be happy to supervise. Open topics are topics proposed by the applicant. For details of our current PhD scholarships please click here

PhD projects currently underway are as follows:

Crime, place and the internet

Crime is known to cluster in time and space. Methods for space-time clustering analysis of crime include Kernel Density Estimation, Dual Kernel Density Estimation, and Local and Global Moran’s I Index, to name a few. The applicability of these methods is possible because the four most common sub-concepts of the physical space – place, distance, size, and route – are well understood. For this reason, we are able to locate crime “hot-spots” and thus inform effective place-based crime prevention policies and strategies.

Nevertheless, the sub-concepts of space – place, distance, size, route – are not the same in the cyberspace as in the physical space. Therefore, space-time clustering analysis of cybercrime requires new means by which place, distance, size, and route can be conceptualised and measured in the cyberspace. The aim of my research is two-folded. First, I aim to develop a framework that can measure the cyberspace and map cybercrime with reference to both the physical and virtual space. Second, I aim to demonstrate how the framework can be implemented to conduct space-time clustering of cybercrime both in the physical and online space. The findings of this research study can inform and provide the security industry and policy-makers with effective deployable solutions to targeting cybercrime.

PhD Researcher: Octavian Bordeneau  
PhD Supervisors: Dr Toby Davies, UCL Security and Crime Science and Dr Gianluca Stringhini, UCL Computer Science.
Contact: octavian.bordeanu.16@ucl.ac.uk

PhD start year: 2017 


Living in the most digitally exciting times with technologies that introduce a smarter life and bring a faster industrial revolution, the new generation of criminals are inevitably becoming more ‘tech savvy’ than ever before. Advances in biology makes even more exciting technologies such as DNA sequencing and engineering publicly available and accessible, regardless of technical background. This highlights the urgency of trying to understand and foresee potential illicit activities that are possible with these rapidly developing technologies.

We continue to face the ongoing challenge of cybersecurity as a result of the launch of the internet in the public domain without any pre-planned defence systems against cybercrime and we are yet to see another paradigm shift in crime. The future challenge from the commercialisation of various biological techniques and the potential “Bio-crimes” that will inevitably appear, must be understood in order to put security systems in place.

This research project will overview the technologies that are catalysing this paradigm shift of crime. The social changes shaped from these technologies will then be explored to identify the new opportunities of offending they produce, here referred to as “Bio-crime”. The aim is to determine how these emerging trends might best be identified at the earliest possible stage before they escalate. Data science techniques will be predominately used to predict how, when and what form these new opportunities will have. Can we predict and defend ourselves from the new offending opportunities?

PhD Researcher: Mariam Elgabry 
PhD Supervisors: Prof Shane Johnson, UCL Security and Crime Science and Dr Darren Nesbeth, UCL Biochemical Engineering.
Contact: m.elgabry.17@ucl.ac.uk

PhD start year: 2017

Cybercrime risks to London’s future street infrastructure 

In December 2013, the Greater London Authority published a plan outlining how the creative power of data and technology will be used to improve the infrastructure of London and establish “Smart London”. Smart cities incorporate technological advances such as autonomous vehicles, smart street lightings, smart solar charger points, networked lamp-post sensors for environment data collection, smart CCTV applications and smart trash cans to name a few. The intelligent devices within the street infrastructure are inherently insecure and have the potential to launch cybercrimes, posing a real threat to the society. The UK Government’s vision for 2021 is to ensure security and resilience to cyber threats as the country progresses to become more digitised. There is also a focus on future proofing London until 2050 so that London retains its place as one of the world’s leading cities.

The aim of the proposed research is to gather the holistic view of the overall smart street infrastructure operating model and its resilience to possible types of cybercrimes in the future smart city of London. The expected outcome of this research is to evaluate possible gaps and risks to cyber security in the operational management of London’s future smart street infrastructure. The long term goal is to study the ongoing effectiveness of cyber security measures on smart London streets through regulations and policies. This research can be extended to other leading cities of the world expecting to implement the smart infrastructure. 

PhD Researcher: Meha Shukla
PhD Supervisors: Prof Shane Johnson, UCL Security and Crime Science and Prof Peter Jones, UCL Civil, Environmental and Geomatic Engineering.
Contact: meha.shukla.17@ucl.ac.uk

PhD start year: 2017

The effects of cyberweapons

The threat landscape relating to cyberweapons and cyber warfare is increasingly dominated by vulnerabilities in internet-connected devices, vehicles, implants, and infrastructure; industrial control systems; and widely-used protocols and applications. The incidence of such vulnerabilities being exploited by criminals, terrorists, and hostile states continues to increase, and the sophistication and ambition of threat actors is escalating rapidly.

Despite these developments, however, the current discourse on cyberweapons and cyber warfare tends to focus heavily on the implications for technical infrastructure, the economy, and wider issues such as policy and legislation, without considering the physical and psychological impacts on humans. 

My research focuses on these effects. There are two major motivations for this; first, to contribute to tactical, policy, and legislative frameworks relating to cyberweapons; and second, to develop robust countermeasures for such effects - thereby providing responders and investigators with effective mitigation strategies, and reducing the threat posed by cyberweapons.

PhD Researcher: Matt Wixey
PhD Supervisors: Prof Shane Johnson, UCL Security and Crime Science and Dr Emiliano De Cristofaro, UCL Computer Science
Contact: matthew.wixey.17@ucl.ac.uk

PhD start year: 2018

Detecting emerging crimes using data science techniques

The internet provides vast amounts of information. More and more people engage in online activities through social media platforms (Facebook, Twitter etc.) or online markets (e-bay, Amazon etc.). Such environments enable individuals with malicious intents to affect massive amounts of people. Authorities have the problem of finding such individuals. The amount of data available is simply too much to be analyzed by humans alone. However, the current information technology enables proficient analyses and inferences from big data, which can support authorities in their work.

Automated methods, such as machine learning (ML) or natural language processing (NLP) techniques are a viable solution to that problem. These techniques are able to meaningful analyze enormous amounts of data. NLP methods can extract grammatical or semantic information from text. For example, finding linguistic commonalities of fraudulent advertisements can be utilized to train ML classifiers, which can then categorize advertisements in being fraudulent or non-fraudulent. With the help of such techniques possible emerging crimes can be uncovered, which would be otherwise not possible.

It is aimed to utilize such automated methods to support authorities in their work with huge amounts of data. Gathering insights from companies and authorities from past crimes will help to establish criteria, which the automated methods will operate on. The goal of this research is to combine human knowledge and data science techniques to find potential emerging crimes, support future human decision making and find ways of preventing new crimes.

PhD start year: 2018
PhD Researcher: Felix Soldner
PhD Supervisors: Prof Shane Johnson, UCL Security and Crime
Science, Dr Bennett Kleinberg, UCL Security and Crime Science
Contact: felix.soldner.18@ucl.ac.uk

Addressing Probable Child Sexual Abusers and Victim Profile Characteristics on Instagram

The use of the Internet and online Social Networks (OSN) has increased drastically in recent years, and increased exposure of children to Child Sexual Abuse (CSA) has followed. However, few empirical studies have been conducted to address the factors that contribute to exposing children to child sexual abusers online and to identify the characteristics of the individuals who contact them. Recent studies have demonstrated that child sexual abusers come from various demographic backgrounds and that it is a challenge to describe a ‘typical’ offender. Studies have also shown that girls are more exposed to CSA attacks than boys but identified no differences in victim ethnicities.

Based on Routine Activity Approach (RAA), my PhD research extends the published work by introducing a methodology to explore the online social communities where probable offenders and children interact and investigating the characteristics of both, as well as any associations between them. The knowledge obtained from this experiment could be used by many private and public sectors such as: law enforcement, child protection entities, school teachers, social media platform developers, parents and children.

PhD start year: 2018

PhD Researcher: Somaya Ali

PhD Supervisors: Prof Richard Wortley, UCL Security and Crime Science, Dr Jyoti Belur, UCL Security and Crime Science

Contact: somaya.ali.17@ucl.ac.uk

Identifying opportunities for crime prevention in smart cities and evaluating their social acceptability

The use of smart city technology is growing rapidly around the world and it is becoming a reality of our daily lives. While most of the technologies employed in such systems already exist in various other contexts (e.g. cameras or audio sensors), it is the depth of interconnectivity and the use of vast amounts of data that are key to the idea of a smart city. In addition to the advantages these technologies offer for many urban challenges such as transportation, waste management, and environmental protection, they also create new opportunities for crime prevention now and in the future.

However, new security technologies may cause controversy because of the threat they can pose to personal data and privacy, which can lead to a lack public support, making them fail in the long-term. This can have serious consequences for the companies designing them, the end-users who employ them, the governments who authorise them, and the citizens whose security or personal data may be compromised.

Ultimately, the goal of the proposed research is to identify how new smart city technologies may be used for crime prevention and identify possible obstacles to their implementation. Special emphasis will be placed on how different technologies are perceived and with what level of public support they are met. In addition, the study aims to test to what extent public perception of smart city interventions correlates with risks previously identified by practitioners. Overall, the study aims for practical applicability by identifying specific interventions and criteria for their '(social) acceptability', laying the groundwork for their future implementation in the United Kingdom. PhD start year: 2018

PhD Researcher: Julian Laufs

PhD Supervisors: Dr Herve Borrion, UCL Security and Crime Science, Prof Ben Bradford, UCL Security and Crime Science

Contact: julian.laufs.18@ucl.ac.uk

Low energy X-ray backscatter imaging for non-destructive evidence harvesting

When X-rays interact with a material, one process that can occur is inelastic or Compton scattering where the X-ray loses some energy to an electron in the material and consequentially changes direction. Sometimes the X-ray will be scattered in the backwards direction. The probability of an X-ray scattering in a direction which is useful for backscatter imaging is dependent on the incident X-ray energy and the density of the material it interacts with.

This relationship is described by the Klein-Nishina equation but, importantly, the probability of an X-ray scattering in the backwards direction is greatest for low energy X-rays interacting with low density materials (i.e. organics). This makes X-ray backscatter imaging a useful technique for investigating surface contaminates (e.g. oils, biological material, drugs, explosives, etc.) and features. For example, it may be possible to build a single-sided imaging system that could be used as a non-contact, non-destructive tool for recording fingerprints, particularly on surfaces that are difficult with current techniques (e.g. textured surfaces).

This project is about investigating the technical trade-offs associated with X-ray backscatter imaging for the purpose of scene-of-crime evidence collecting. I will be designing and building a lab based experimental setup to develop an understanding of how the X-ray generator/detector geometry and settings can be adjusted to optimise the technique. The project will also investigate how information is affected by background substrate, X-ray energy and detector characteristics.

PhD start year: 2018

PhD Researcher: Samyog Adhikari

PhD Supervisors: Dr Robert Moss, UCL Medical Physics, Dr Georgina Meakin, UCL Security and Crime Science

Contact: samyog.adhikari.17@ucl.ac.uk

Guarding against Adversarial Perturbation in Automated Security Algorithms

Deep Learning algorithms can detect and classify people, activities and objects in images with performance at human-level. These methods are finding their way into consumer products. They also offer great potential in security applications for example in person verification at checkpoints, suspect-finding in video, discovering harmful content on the internet, and detecting threats in bags and parcels. However, they have a curious vulnerability ('adversarial perturbation'), which while harmless for consumer applications offers a potential exploit for determined adversaries in the security realm.

An adversarial perturbation is a very small, but very precise, change to the image input into a classifier that causes the classifier output to dramatically change. For example, with just the right perturbation an image of a cat can be mis-classified as a dog, while still looking like a cat to a human viewer. In the context of security this method could allow an adversary to, for example: alter harmful video content to escape detection by automated methods; conceal threats in bags; or maliciously 'place' targeted individuals into pornographic content, so far as face-based search algorithms are concerned. At present there is no known fix for adversarial perturbations. Can this problem be fixed, or is it an issue that we need to learn to live with and safeguard against in other ways? This PhD will address this problem before adversaries develop the sophistication to exploit it.

PhD start year: 2019 (April)

PhD Researcher: Maximilian Mozes

PhD Supervisors: Dr Lewis Griffin, UCL Computer Science, Dr Bennett Kleinberg, UCL Security and Crime Science

Contact: N/A

Horizon scanning through computer-automated information prioritisation

t is no secret that police forces are seeking to advance their repertoire of analytical and predictive tools to deal with emerging crimes as they adapt to the repercussions of penurious fiscal policies. However, the volume, variety and velocity of data both recorded by police forces and available through open sources means that analysing such data can be an ambitious and challenging undertaking. Despite the aforementioned obstacles horizon scanning is set to become a prominent aspect of future policing allowing police forces to identify emerging threats, anticipate imminent crimes and to extinguish future methods of perpetration. In essence, allowing the police to stay one step ahead of criminals.

In my PhD I will aim to develop working relationships with law enforcement organisations to resolve current blockades in horizon scanning. The first stages of my PhD will be to gather information on the structural and relational limitations of the multifarious systems used by police forces. Using this information, and a selection of data science techniques, I will develop automated tools that can identify and prioritise emerging trends in vast amounts of data using natural language processing and machine learning; whilst being able to communicate these results appropriately to the relevant users and stakeholders by employing suitable interactive data visualisations. As my research develops I aspire to incorporate different data sources and types (for example, employing computer vision and deep learning on social media posts) into large scale threat scanning models.

PhD start year: 2018

PhD Researcher: Daniel Hammocks

PhD Supervisors: Dr Bennett Kleinberg, UCL Department of Security and Crime Science and Professor Kate Bowers, UCL Department of Security and Crime Science

Contact: daniel.hammocks.18@ucl.ac.uk

Refugee flows and instability

The massive flow of refugees from conflict zones since the Syrian Civil War has presented global society with growingly complex problems related to security and economic stability. The crisis is well reflected in the annual report of UNHCR according to which 42,500 people are displaced everyday from conflict zones. United Nations Refugee Agency reports 65 million people in the world are currently considered as refugees who are looking for asylum and this has triggered a global security issue.

The idea that a quantitative approach, borrowed from complex systems literature, is an essential tool to study the network of refugees and their movements, engaged me in this project. In the same spirit, mathematical models, computer simulations and statistical physics measures are the remarkably useful tools to assess the current policies such as border closures and detention centres which are currently affecting lives of thousands of refugees everyday all around the Mediterranean sea and other areas.

My project aims to investigate why some people flee from conflict zones, where they go and how these movements impact the stability of host countries and the entire global security. The project studies this phenomenon at both microscopic and macroscopic scales. We study and model how an individual’s decision in conflict zones is affected by the small network of people around him as well as other factors such as social media, language, religion, and the capacity of host countries.

Climate changes, conflicts, and many other factors reveal how irregular migration is not going to remain limited to this scale. Thereby, predictive models are growing as a subject of notice to contribute to some predictive tools in the future. We aim to investigate the patterns of movements on global scale and produce predictive models, according to the data we have from refugees in Lebanon and other data sets made by the UNHCR, by exploiting network theories and statistical learning approach. This approach enables us to forecast the movements and it consequently leads to a set of prudent policies to tackle the refugee crisis and support this group of migrants. 

PhD start year: 2018

PhD Researcher: Zahra Jafari

PhD Supervisors: Prof Shane Johnson, UCL Security and Crime Science, Dr Toby Davies, UCL Security and Crime Science

Contact: zahra.jafari.17@ucl.ac.uk


General outputs from the Dawes Centre can be found below. Outputs from individual research projects will be published on the pages for those projects (see above) or on the PhD sections in the case of outputs from PhD projects.  

Each year the Dawes Centre publishes an Annual Report highlighting some of its key activities during the year. We encourage you to take a look at the report below.