PhD Research

Can we predict new and evolving crimes? And if we can predict them, can we prevent them?

Below you can read about the PhD projects currently underway and being funded by the Dawes Centre for Future Crimes.

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 start year: 2017 
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

Outputs: 

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 start year: 2017
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       https://medium.com/@mariamelgab        http://uclsecretsociety.org/mariamelgabry/           https://www.linkedin.com/in/mariam-elgabry/       

Outputs: 

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 start year: 2017
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

Outputs: 

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 start year: 2018
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

Outputs: 

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   http://fsoldner.net/    

Outputs:

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

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 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 https://urbanviolence.org/user/julian.laufs/ 

https://shoc.rusi.org/members/julian-laufs   

Outputs: 

International anti-money laundering and counter-terrorist financing (AML/CTF) efforts have grown exponentially since the 1990s. AML/CTF is now a key element of financial services, combining obligations set by both domestic and supranational organizations. However, these detection efforts are consistently increasing in implementation costs, while failing to keep up with the latest money laundering and terrorist financing risks. New payment methods are providing new opportunities for illicit transactions, while cryptocurrencies continue to pose new risks by increasing the anonymity of their users. Contemporary prevention initiatives are struggling to keep up with rapidly improving criminal sophistication.

This project aims to apply a crime scripting approach to detecting the likelihood, nature and scale of suspected money laundering or terrorist financing offences. This involves understanding the motives, constraints and indicators involved in each stage of the offence to potentially predict the previous or subsequent stages. In doing so, the prevention, resource allocation and successful investigation rates of law enforcement agencies can be improved. The project includes a scoping review, a Delphi Study to identify future typologies based on projected risks, and a subsequent scripting exercise of past cases to explore whether crime scripting can be an effective means of contributing to contemporary AML/CTF efforts.

PhD start year: 2019
PhD researcher: Eray Arkatuna
PhD supervisors: Prof Shane Johnson, UCL Security and Crime Science; Dr Amy Thornton, UCL Security and Crime Science
Contact: eray.akartuna.17@ucl.ac.uk

Outputs:

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: Prof Lewis Griffin, UCL Computer Science, Dr Bennett Kleinberg, UCL Security and Crime Science
Contact: maximilian.mozes.18@ucl.ac.uk   http://mmozes.net/

Outputs:

It 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: Professor Kate Bowers  and Dr Bennett Kleinberg , UCL  Security and Crime Science
Contact: daniel.hammocks.18@ucl.ac.uk

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

Outputs:

The rise in popularity of cryptocurrencies since the release of Bitcoin in 2009 has changed the face of financial fraud, facilitating lower-risk anonymous money laundering and fraudulent transfers on a massive scale. Methods of detecting, mitigating and preventing financial fraud remain underdeveloped relative to the value of the cryptocurrency market, and the efforts of academics, law enforcement and policymakers remain in their infancy. This project will adapt data science-based fraudulent transaction detection methods used in traditional financial services to the cryptocurrency market to ascertain their effectiveness in the cryptocurrency space. The results of this project will provide an empirical basis for policymakers to develop evidence-based legislation surrounding digital currencies worldwide, as well as provide a necessary contribution to this currently sparse body of academic literature. It will also enable innovation, facilitating the entry of conventional financial services companies into the cryptocurrency arena by providing a method for conducting due diligence on these transactions in the absence of accepted anti-money laundering processes.

PhD start year: 2019
PhD student: Arianna Trozze
PhD supervisors: Dr Toby Davies and Dr Bennett Kleinberg, UCL Security and Crime Science
Contact: arianna.trozze.19@ucl.ac.uk

Outputs:

Anomaly detection is the task of identifying items or events which deviate significantly from normal appearance or behaviour. This is a well-established approach in financial fraud detection, but is applicable in many other areas within the security domain. In X-ray screening of baggage and cargo it can be used to detect concealment, even if a threat is not directly recognizable. In biometric verification it can be used to detect tampering, such as facial morph images which match two identities. In home security it can enable advanced alarm systems that warn when unusual physical or digital activity is occurring. 

Anomaly Detection is a sub-problem of machine learning. It is common for machine learning applications to be hindered by a lack of sufficient available training data. This problem is raised to its most extreme form in anomaly detection, where there may be no available example anomalies. The answer, to be explored in this PhD project, is to use methods of self-supervised rather than supervised learning. Self-supervised learning uses proxy tasks which can be defined on normal data, allowing effective data representations to be learnt from that data alone, rather than on the contrast between normal and threat data. Effective representations allow effective modelling of the distribution of normal data so that anomalous deviations can be spotted. 

The goal of the PhD will be to develop self-supervised learning methods that are effective for anomaly detection. The first problem to be worked on will be anomaly detection in X-ray security imaging. Other problems that we plan to explore are anomaly detection in audio (for deep fake detection), in video streams (as an AI-driven flexible home emergency/crime alarm), and in text (for detecting phishing, and similar, emails).
 
PhD start year: 2019
PhD Student: Kimberly Ton-Mai
PhD Supervisors: Prof Lewis Griffin, UCL Computer Science and Dr Toby Davies, UCL Security and Crime Science 
Contact: kimberly.mai@ucl.ac.uk

Outputs:

This Phd will investigate the levels of threat that “deepfakes” pose with regard to various contexts within human society, by means of conducting a human user study and testing to find contexts where humans are deceived by these deepfakes and whether any particular types of humans are particularly easily deceived. The main context to be studied will be news propagation. After establishing the various levels of threat posed by deepfakes, the project will aim to propose protection systems for the UK’s mainstream news propagation networks, such that the UK might lead the way in protecting its news systems and journalists against the possibility of “deepfake news injection”. The context of mainstream news propagation networks in the UK is one that is heavily adverse to outside regulation. The freedom of the press is important. However, with research evidencing that deepfakes are a threat to an array of important stakeholders via the context of news propagation, measures can be taken to implement such fixes as are necessary whilst leaving journalistic liberties intact. 

PhD start year: 2019 
PhD student: Sergi Bray
PhD supervisors: Prof Shane Johnson and Dr Bennett Kleinberg, UCL Security and Crime Science 
Contact: sergi.bray.18@ucl.ac.uk

In criminal investigations (street samples, biological fluids, gunshot residues, etc.) efficient and accurate methods are needed for detection and analysis of evidence. The crime scene offers many analytical challenges due to its complexity and therefore requires the use of a variety of techniques to assess evidence. Current technologies have issues of low specificity, detrimental effects on evidence recovery and an inability to be performed simultaneously. Biosensors can be applied in the detection of biomolecules and biological components (fingerprints, blood samples, odours etc.) found at crime scenes, which can aid in identification and tracking of suspects. The application of biosensors represents a significant advancement for forensic sciences with the opportunity for untrained individuals in the field to carry out economic, rapid and decentralised testing of complex samples. 

The recent rapid development in the research and development of biosensors is due mainly to advances in nanomaterial-based biosensors with advantages of rapid response time, high stability, superior biocompatibility and low cost. Despite the demonstrated versatility of design and usefulness in their potential for analysis of biological fluids and multiplexed determinations, biosensors in forensic analysis are less advanced than in other fields. 

This project aims to explore this lack of positive identification and on-site testing by improving the application of biosensors, and their current vast development in other sectors, to forensics. Therefore, a systematic review of current technologies and their potential for combating crime is to be undertaken. These findings and further discussions with police forces and other stakeholders will determine appropriate biosensing technologies for further development. The overall aim of the project is the fabrication of a field-deployable biosensing device to combat crime.

PhD start year: 2020 
PhD student: Alice Cozens
PhD supervisors: Prof Kwang-Leong Choy, UCL Institute of Materials and Prof Shane Johnson, UCL Security and Crime Science
Contact: alice.cozens.20@ucl.ac.uk

The nature of the international security environment is changing in the light of hybrid challenges. Although the concept of hybrid threats is not new, it has recently gained wider traction among Western countries due to the foreign interventions in Ukraine in 2014 and during the 2016 United States presidential election. The evolution of hybrid threats has been driven by the rise of the cyber domain and online information spaces. The potential deployment of cyberattacks against the target and the use of social networking services to conduct influencing activities are important attack methods for hybrid threats.

Since 2016, NATO and the European Union have decided that countering hybrid threats is a priority for cooperation. An important goal for the world's governments and international agencies is to respond to hybrid threats. Deciding on the appropriate and proportional response is difficult due to the complexity of the concept and underdeveloped methods for assessing the impact of hybrid threats. The aim of the research is to propose a method that helps to quantify the impact of hybrid threats. The results of the research will provide empirical basis for justifying response decisions and important contributions to the sparse body of existing literature.

PhD start year: 2020
PhD student: Kärt Padur
PhD supervisors: Prof Stephen Hailes, UCL Computer Science and Dr Herve Borrion, UCL Security and Crime Science
Contact: kart.padur.20@ucl.ac.uk

Smartphones and laptops play increasingly essential roles in many people's daily lives. Studies find that the average adult in developed areas spends at least 4 hours every day looking at a screen. This makes online deceptions such as phishing and disinformation on social media increasingly attractive means for cybercriminals. Such attacks prove to be effective since they are easy to scale and cybercriminals can more easily hide their true identities from local law enforcers. A big phishing attack from 2015 for instance defrauded Facebook and Google for millions of dollars. Hence, it is important to understand why individuals fall for online deceptions and what individual differences and contextual factors may make certain people more vulnerable than others.

In my research I aim to test if we can let computers automatically detect when individuals are particularly susceptible to online deceptions. If so, the goal is to develop interactive user interfaces that help to shield people from falling for such frauds, by dynamically changing the appearance of online environments. To this end, I use an eclectic mix of psychophysiological methods, web application development and machine learning.

PhD start year: 2020
PhD student: Sarah Zheng
PhD supervisors: Prof Tali Sharot, UCL Experimental Psychology and Dr Ingolf Becker, UCL Security and Crime Science
Contact: sarah.zheng.16@ucl.ac.uk

The advent of a global pandemic has had numerous consequences on people and, more in general, on society. During this year of isolation and/or reduced social interactions, society turned toward technology to find a substitute and a tool to stay engaged and entertained. During this period level of stress, isolation and depression, also due to a looming economic crisis, have reached new highs, affecting, in particular, the most endangered and exposed parts of society. Law enforcement agencies and charity group around the UK have advocated and highlighted that this pandemic will affect in particular young and vulnerable individuals. 
 
Hence, this PhD research aims to look at the consequences and effects of human trafficking during this pandemic and the adaptability of this illicit crime enterprise to the new era of digitalisation and the high volume of vulnerable individuals. Feelings of isolation and an economic crisis are very dangerous when combined and easy to be exploited by traffickers. Thus, this project aims to find answers to whether and how criminal enterprise have adapted in order to reach these individuals world-wide; to understand if human trafficking has reached a new level of sophistication by incorporating technology in their business model. By focusing on modus operandi, psychological manipulation techniques and decision-making in relation to technological engagement, this research aims to look at the way digital interaction occurs from an offender point of view. 

PhD start year: 2020
PhD student: Francesca Costi
PhD supervisors: Professor Kate Bowers and Dr Sanaz Zolghadriha, UCL Security and Crime Science
Contact: francesca.costi.19@ucl.ac.uk

Brexit marks the first time in history that a state has withdrawn its membership from the European Union.  This has presented one of the most important political rearrangements on the European scene since the fall of the Berlin Wall. While debates regarding the impact of Brexit on economic issues took the centre stage during the lead up to the Brexit referendum as well as during the withdrawal process, much less attention was paid to its impact on crime and security. At a time when major serious crimes are increasingly transnational in scope, the UK will face limited access to the EU’s security and criminal justice infrastructure.  At the same time, growing asymmetries between EU and UK laws and policies may create new criminal opportunities for serious and organised crime.  

The objective of this PhD project is to create a coherent analytical product outlining alternative future crimes to guide strategic planners and decision-makers in designing robust policies capable of addressing various future scenarios and thus disrupting opportunities for criminals before they can manifest in full form. It will do so by, firstly, conducting a systematic review to examine the state-of-the-art in knowledge concerning the impact of Brexit on policing and serious crime in the UK, and secondly, by utilising foresight methodologies to identify types of opportunities for serious and organised crime which are likely to emanate from the social, political and legal changes caused by Brexit.  

PhD start year: 2020
PhD student: Jakub Pinter
PhD supervisors: Prof Shane Johnson and Dr Manja Nikolovska, UCL Security and Crime Science
Contact: jakub.pinter.20@ucl.ac.uk

Smart cities take the principles of smart devices and apply them at the scale of making cities more efficient and sustainable. This promise of efficiency means some environments may become increasingly automated and rely less on the physical presence of security guards and place managers. Research shows guardianship, including formal and natural surveillance, plays a clear role in the disruption of offending and is key to preventing crimes like hostile reconnaissance. Smart cities will generate potential opportunities for crime, but also generate disruption opportunities. This project will seek to understand the affordances and potential detriments a move toward smart cities has on criminal and terrorist attack planning (including hostile reconnaissance) and targeting. For example, place managers, such as bus drivers, parking lot attendants, train conductors and others, perform a surveillance function by virtue of their position of employment. Place managers may prevent crime because potential offenders are deterred by their increased subjective probability of being detected. These forms of surveillance may also increase the true probability of detection. If the amount of place managers declines, how can this role be incorporated into smart systems? 

PhD start year: 2020
PhD student: Phillip Doherty
PhD supervisors: Dr Paul Gill and Dr Sanaz Zolghadriha, UCL Security and Crime Science
Contact: philip.doherty.16@ucl.ac.uk

This project seeks to outline the imperative for further research, on the evidential opportunities innate to social media usage by young persons engaged in organised criminal conduct. The phenomena of county line gangs (CLG) has received significant media attention. However, the criminal investigation techniques deployed by practitioners have undergone less public scrutiny. Social media intelligence, also known as internet intelligence investigations (III), is the operational tactic used by law enforcement organisations (LEO) to collect evidence from suspects and victims in a wide range of criminal investigations. This is a crucial intelligence development tool for organised crime investigations involving child criminal exploitation (CCE). Due to the pertinence of online dependence on communication and social networking for all young persons, which also encompasses county line gangs. Internet Intelligence & Investigations will be examined as a distinct form of digital forensic evidence that will be a key technique in the effective enforcement and disruption of internet enabled crime perpetuated through existent and emerging social networking sites. 

PhD start year: 2020
PhD student: Kane Brooks
PhD supervisors: Professor Kate Bowers and Dr Sanaz Zolghadriha, UCL Security and Crime Science
Contact: kane.brooks.20@ucl.ac.uk

High-profile scandals, data breaches, and daily cookie consent notices, have gradually raised the public's awareness to the potential use and misuse of their personal data.  Data is the new oil and Big Data has created Big Tech and new business models. Privacy and data protection are important because the continued growth of the internet is predicated on a business model that harvests consumers' data to generate targeted advertising revenue that in turn funds the growth of the information industry. I am researching the intersection of technology, business, and public policy into how Big Tech can better accommodate consumer's privacy concerns in a win-win model.

PhD start year: 2020
PhD student: Gerard Buckley
PhD supervisors: Dr Ingolf Becker, UCL Security and Crime Science and Dr Tristan Caulfield, UCL Computer Science
Contact: gerard.buckley.18@ucl.ac.uk

Decision-making processes once delegated to humans are progressively mediated, if not even determined, by machine learning algorithms. Machine learning algorithms are powerful socio-tech constructs which may however raise not ethically neutral outcomes. Examples nowadays abound, with the consequence that the ethical debate has gone mainstream. With over 80 AI ethics guides available in the public domain, the debate has primarily focused on principles - the ‘what’ of AI ethics. Hence, this PhD research aims to advance the question of ‘how’ to reach the ‘what’ when machine learning is employed in high impact and socially sensitive contexts, like the child protection system.

PhD start year: 2021
PhD student: Aliai Eusebi
PhD supervisors: Dr Enrico Mariconti, Dr Ella Cockbain, UCL Security and Crime Science, and Dr Marie Vasek, UCL Computer Science.
Contact: aliai.eusebi.16@ucl.ac.uk

The online purchase of illicit substances has been happening on the dark web, but in recent years commercial activity has moved to popular social media apps. Teenagers can now buy and sell illicit substances through Instagram or Snapchat, without having to physically leave their homes.

In a couple of clicks, posts advertising the sale of cannabis, cocaine, or ecstasy can be easily found. This has led to an increasing number of teenagers dying of overdose, given the accessibility and perceived trust when buying drugs through social media.

My PhD project has 3 key objectives: to measure the extent of this issue cross-nationally, to evaluate the effectiveness of preventive solutions and to successfully implement these within policy strategies. This research will be using various disciplinary frameworks, including social sciences, public policy, crime and computer science methods.

User-generated data and young people’s views will be combined to effectively detect and counter the harm caused by the advertisement and sale of drugs. Collecting cross-national data on terminology used to bypass detection such as emojis is crucial for social media companies to better target these posts and improve reporting systems. The findings of this research will enable to propose implementable policy solutions in the aim of cross-national and institutional collaboration among concerned actors.

PhD start year: 2021
PhD student: Ashly Fuller
PhD supervisors: Prof. Shane Johnson, Dr. Enrico Mariconti, UCL Security and Crime Science, and Dr. Marie Vasek, UCL Computer Science.
Contact: ashly.fuller.16@ucl.ac.uk

In 2018 technology-facilitated abuse was marked by Comic Relief and the charity SafeLives as an emerging concern. Following the work of teams that include the G-IoT project at UCL, there is work taking place that explores this prevalent form of abuse. However within the Violence Against Women and Girls (VAWG) sector this form of abuse has only recently begun to be acknowledged as a risk indicator. Efforts thus far within the industry in response have included dedicated tech abuse caseworkers and device how-to guides.  Sector-wide understanding, sharing of learning, and assessments to develop evidence-based responses in the United Kingdom remain few and far between. This PhD seeks to understand the prevalence of technology-facilitated abuse in the context of intimate partner relationships. Key areas of focus will include; the understanding of tech abuse within the Domestic Abuse sector, how the sector assesses this form of abuse, what implementations are currently being utilized to support survivors of tech abuse, and could this form of abuse be considered a high-risk of harm indicator. It is hoped that through effective research and collaboration with the Domestic Abuse sector researchers, policymakers, and service providers that a better understanding of technology facility abuse can be achieved. 

PhD start year: 2021
PhD student: Ademelza Penaluna
PhD supervisors: Dr Leonie Maria Tanczer, Lecturer in International Security and Emerging technologies, and Professor Shane Johnson, Director, Dawes Centre for Future Crime at UCL.
Contact: demelza.penaluna.21@ucl.ac.uk

Internet of Things (IoT) devices are now ubiquitous with most parts of day-to-day life. We use them to monitor our health and wellbeing through devices such as Smart Watches. We use them for entertainment purposes such as using Smart TV’s and Games Consoles. We use them to monitor household energy consumption through Smart Meters. We also use them to power our cars through Smart Vehicles. In addition, we also now use them to monitor individuals that approach our properties using Smart Cameras/CCTV and Smart Doorbells. The applications for IoT devices are limitless and they provide amazing benefits to our way of life. However, ordinary consumers do not consider the potential dangers to these devices that can occur through present and future cyber qttacks and the types of heinous and sometimes dangerous crimes that can be committed using these IoT devices as attack vectors through attacks such as Man-in-the-middle, Replay Attacks, etc. As such this topic aims to conduct a review of the potential attacks that can be committed on these devices and then to conduct a systematic testing methodology to identify what attacks are possible and list potential crimes that can be committed such as cyber stalking, household burglaries, etc.

PhD start year: 2021
PhD student: Ashley Brown
PhD supervisors: Dr Enrico Mariconti, UCL Security and Crime Science, and Prof Shane Johnson UCL Security and Crime Science.

Contact: ashley.brown.21@ucl.ac.uk  

Small to Medium Enterprises (SMEs) are known to be prolifically targeted by cyber criminals. This is due in part to their apparent inability to counteract the ever-changing cyber threat landscape, along with a potential/ perceived disregard from the decision makers within these organisations to do so. Despite the fact that SMEs account for an estimated 99% of businesses and contribute to 60% of employment, there continues to be a focus on the risks associated with cyber attacks on larger businesses and not how the vulnerabilities within their smaller counterparts can negatively impact on the supply chain. This thesis will therefore aim to outline these vulnerabilities in the context of real business victim experiences of cyber attacks, with this knowledge used to better inform a framework that can be applied by SMEs to protect themselves in the future.

PhD start year: 2022 (Jan)
PhD student: Siobhan McCrea
PhD supervisors: Dr Ingolf Becker, UCL Security and Crime Science, and Prof Shane Johnson UCL Security and Crime Science.

Contact: siobhan.mccrea.18@ucl.ac.uk 

A surge in cyber-threats has resulted in an increasing rate of data breaches within companies, leading to financial and economic impacts across the globe. For decades, cybersecurity efforts have concentrated on large corporations, leaving Small and Medium Enterprises (SMEs) ill-equipped to handle cyberattacks. Yet, SMEs represent a vast majority of the global economy - according to the UK parliament, over 99% of the 5.6 million businesses in Britain are considered SMEs and are responsible for 61% of employment and 52% of the country's turnover. Consequently, SMEs have become attractive targets as they struggle to implement solutions designed for larger organisations with in-house cybersecurity resources. While it is evident that SMEs must address security issues, they lack financial resources, expertise and sometimes awareness to address cybersecurity threats. This research presents a systematic and empirical approach to chart threats faced by SMEs, mapping uptake controls along with challenges and constraints in adhering to cybersecurity practices. The ultimate aim is to provide a tailored set of recommendations to equip SMEs with cyber-resilience.

PhD start year: 2021
PhD student: Carlos Rombaldo Jr.
PhD supervisors: Prof Dr Ingolf Becker, UCL Security and Crime Science; and Prof Shane Johnson, UCL Security and Crime Science
Contact: carlos.rombaldo.21@ucl.ac.uk

According to the Home Office, the proportion of adults aged 75 and over that use the internet has almost doubled from 29% in 2013 to 54% in 2020. This represents a steep increase in the number of potentially susceptible targets accessible by malicious actors online. In fact, older people (60+) are victims of fraud more than any other crime, and there is general consensus that elderly victims can suffer disproportionate psychological distress, not forgetting significant economic losses. The COVID-19 pandemic is thought to have exacerbated this situation, with more vulnerable people, heightened vulnerability, and an additional context with which attackers can frame their approaches.

With particular attention paid to social engineering scams (which are tailor-made to exploit vulnerability and hence highly relevant for the older demographic), my research first seeks to analyse the current cyber vs elderly landscape using primarily the Crime Survey of England and Wales. Subsequently, after identifying the most appropriate problem area, I plan to collaborate with stakeholders from across the banking, cybersecurity, policing and social care sectors in order to design a targeted and practicable response that protects older people from cyber-attacks.

PhD start year: 2022 
PhD researcher: Ben Havers
PhD supervisors: Prof Claudia Cooper, UCL Psychiatry, and Dr Kartikeya Tripathi, UCL Security and Crime Science
Contact: benjamin.havers.20@ucl.ac.uk

 

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