Cyber, Cyber-Physical and Information Security
For further information on Imperial's work in cyber security, including people, please visit the dedicated cyber security section.
Example projects from across Imperial College London
Example projetcs from across Imperial College London
SeReCa: Secure Enclaves for Reactive Cloud Applications
The SERECA project aims to remove technical impediments to secure cloud computing, and thereby encourage greater uptake of cost-effective and innovative cloud solutions in Europe. It proposes to develop a secure environment for reactive cloud application using the new Intel's CPU extension.
PI: Dr. Peter Pietzuch
Machine Learning, Robust Optimization, and Verification.
Part of EPSRC's and Singapore's National Research Foundation's collaboration to address cyber threats. The project investigates new approaches for modelling and optimisation by which cybersecurity of systems, processes, and infrastructures can be more robustly assessed, monitored, and controlled in the face of stochastic and strict uncertainty.
PI: Professor Michael Huth
Reasoning about Lone-Actor Terrorists
Reasoning about Characteristics and Behaviour of Lone-Actor Terrorits: A Logic-Based Approach.
We investigate the use of logic-based reasoning techniques to generate hypotheses about characteristics and antecedent behaviours of lone-actor terrorists and study the influence of specific factors (e.g. social, demographic, economic) on lone actors' behaviour.
PI: Dr D Alrajeh
The NEMESYS project aims to develop novel security technologies for seamless service provisioning int eh smart mobile ecosystems. Mobile and smartphone security is a fast moving field. To this end, NEMESYS gathers and analyses information about the nature of cyberattacks targeting smart mobile devices, so that appropriate countermeasures can be taken to combat them.
PI: Professor Erol Gelenbe
Trust Evidence From Programmers' Intent
This project explores how trust evidence for running software -- but also for more general systems -- can be specified and aggregated in order to support security-relevant decision making. Particular emphasis is being placed on the fact that such evidence may be qualitative as well as quantitative, and that is may cover or interact with a range of modalities such as risk, security, cost, and impact.
PI: Professor Michael Huth
Formal Verification of Treaty Processes
This project, in collaboration with a UK defence company, extends and combines mathematical modelling and verification approaches to make them cope with the inherent lack of available data in the domain of arms control treaty design and implementation. New modelling and analysis methods developed have allowed for a much more sophisticated approach to modelling arms control, and enable a user to optimise treaty-relevant decisions - made during the design or execution phase of a treaty - given various modelling parameters and perhaps even multiple objectives.
PI: Professor Micahel Huth
CIPART: Cloud Intelligent Protection at Run-Time
CIPART investigates the intelligent protection of cloud environments at run-time by integrating security modelling and analysis with run-time system management information and AI techniques for knowledge representation, analysis and learning. Additionally, the project also aims to achieve a better understanding of the business models and incentives involved in the relationships between cloud tenants and hosting organisations in the provision of security services based on measures of cost, risk and value.
PI: Professor Emil Lupu
Naggen: a Network Attack Graph GENeration System
Attack graphs constitute a powerful security tool aimed at modelling the many ways in which an attacker may compromise different assets in a network. Despite their usefulness in several security-related activities, the complexity of these graphs may massively grow as the network becomes denser and larger, thus defying their practical usability. This project looks at novel approaches to deal with attack graph complexity based on core attack graphs.
PI: Professor Emil Lupu
Privacy Dynamics: Learning from the Wisdom of Groups
In recent years, social psychologists have made a core distinction between personal identity and social identity, the formation of which are both dynamic. Understanding the identity process is key to assessing the impact that privacy and security policies have on people’s behaviours. This project studies privacy management by investigating how individuals learn and benefit from their membership of social or functional groups, and how such learning can be automated and incorporated into modern mobile and ubiquitous technologies that increasingly pervade society.
PI: Dr A Russo
Joint modelling of edge evolution in dynamic graphs
The aim of this project is to develop a general and scalable framework for monitoring and unsupervised anomaly detection of large dynamic graphs. Understanding normal behaviour of communication patterns between pairs of individuals or IP addresses is complex, due to the inherent seasonality and burstiness in human behaviour. The challenge of anomaly detection is to find departures beyond such normal behaviour on the vast collection of edges. It is within this context of dynamic graphs that many cyber security problems arise.
PI: Dr N Heard
Detecting Malicious Data Injections in WSNs
Malicious data injections are violations to the measurements’ integrity in a Wireless Sensor Network (WSN), which ultimately prevents delivery of a critical service. We have designed a wavelet-based anomaly detection algorithm which evaluates correlations in context and infers if they can be explained by events.
PI: Professor Emil Lupu
CloudSafetyNet: Data-centric Security for Clouds
The focus of the CloudSafetNet project is to fundamentally rethink how platform-as-a-service (PaaS) clouds should handle security requirements of applications. The overall goal is to provide the CloudSafetyNet middleware, a novel PaaS platform that acts as a ”safety net”, protecting against security violations caused by implementation flaws in applications (”intra-tenant security”) or vulnerabilities in the cloud platform itself (”inter-tenant security”).
PI: Dr P Pietzuch
OPAL: Infrastructure to safely use large-scale mobile data
OPAL is a joint project between Imperial College London and MIT in partnership with Orange, Telefonica, the Data-Pop Alliance and the WEF to develop and deploy secure and question-and-answer-based infrastructure allowing mobile phone data to be used at scale while truly preserving people’s privacy.
PI: Dr Yves-Alexandre de Montjoye
AF-Cyber: Logic-Based Attribution And Forensics In Cyber Sec
The main goal of AF-Cyber is to investigate and analyse the problem of attributing cyber attacks. We plan to construct a logic-based framework for performing attribution of cyber attacks, based on cyber forensics evidence, social science approaches and an intelligent methodology for dynamic evidence collection.
PI: Professor E Lupu
COPES (COnsummer-centric Privacy in smart Energy Meters) deals with privacy risks created by high-resolution smart meter measurements, which can reveal sensitive information about consumer behaviour. The goal is to develop and integrate novel hardware and software solutions to guarantee the privacy of energy consumption behaviour of residential customers.
We are collaborating with the KIOS Research and Innovation Center of Excellence (KIOS CoE), University of Cyprus. The Center was selected by the EU to advance into a Research and Innovation Center of Excellence in 2017. The KIOS CoE is the largest research and innovation center in Cyprus on Information and Communication Technologies (ICT) with an emphasis on monitoring, control, management and security of critical infrastructures.