| EPSRC Reference: |
EP/N02026X/1 |
| Title: |
Cyber security solutions for smart traffic control systems |
| Principal Investigator: |
Tran-Thanh, Dr L |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Sch of Electronics and Computer Sci |
| Organisation: |
University of Southampton |
| Scheme: |
Standard Research |
| Starts: |
31 March 2016 |
Ends: |
31 October 2018 |
Value (£): |
199,167
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Networks & Distributed Systems |
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| EPSRC Industrial Sector Classifications: |
| Information Technologies |
Transport Systems and Vehicles |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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14 Oct 2015
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Singapore-UK Cyber Security
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Announced
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Summary on Grant Application Form |
We aim to develop a solution framework that can efficiently tackle the cyber security vulnerabilities of smart traffic control systems. Such solutions would be very beneficial for both the industry and society, as current Internet of Things (IoT) based networks, which enjoy significant interest as a key technology towards the development of smart societies, are typically very vulnerable against cyber attacks. This is especially true in the case of traffic systems, as they are among the key national strategic infrastructures that have to be protected at the highest security level.
This proposal is the first study that aims to propose a solution to these cyber security challenges. In particular, we believe that such efficient solutions need to meet the following criteria:
1. Human-agent heterogeneity: The system design has to be capable of dealing with the heterogeneity of interactions and information sharing between different participating devices (i.e., agents) and humans within the traffic control system.
2. Robustness: the system has to be resistant against different major attack scenarios that may significantly vary in both motives and execution. Adversaries could be very strategic in planning sequential attack actions.
3. Adaptivity: The defence mechanisms should react in real time, in an online manner, and adaptive to the concrete actions of the attackers.
4. Limited resources: The defence strategies have to take into account that there are typically limited resources available to execute each step, or decision made during the process.
To address these criteria, we propose a solution that relies on three fundamental components: (i) human-agent collectives based framework; (ii) game theory; and (iii) resource-constrained online machine learning. In particular, the human-agent collectives based perspective allows us to build a unified framework for smart traffic control systems that can efficiently deal with the heterogeneity between different participating agents and humans. We then use security game theory to discover and analyse major attack scenarios, in order to make our system design robust against them. We then apply resource-constrained online machine learning algorithms to develop efficient adaptive and real-time defence mechanisms. Additionally, these mechanisms also consist of further game theoretic approaches that can be used to predict the strategic behaviour of the attackers, and thus, can make more efficient decisions. Finally, we will build a real testbed as a proof-of-concept of our proposal.
There are many advantages of this solution, namely:
1. The unified human-agent collectives based framework will enable us to define formal descriptions and categorisations of interactions within the system in a principled manner. This will provide a strong basis to develop methodologies to identify and analyse suspicious behaviours.
2. With the game theoretic framework, we will be able to investigate the worst case scenarios of different attack types. This will provide us a full understanding of what should and should not be taken into account during the design of the system.
3. The game theoretic framework also provides an efficient tool to analyse the strategic behaviour of the attackers. This knowledge will play an essential role in predicting the possible future actions of the attackers.
4. The online defence mechanism combines the extracted knowledge, provided by the strategic behaviour analysis within the human-agent collectives and game theoretic frameworks, with efficient machine learning techniques to quickly and efficiently detect malicious behaviours.
5. Given the available resources, the defence mechanism then can decide what is the best response actions that can either prevent the malicious user to cause any harm (in case of sufficient resources are available), or to minimise the damage the attacker can cause (in case of having restricted resources).
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.soton.ac.uk |