| EPSRC Reference: |
EP/R011494/1 |
| Title: |
DeepSecurity - Applying Deep Learning to Hardware Security |
| Principal Investigator: |
O'Neill, Professor M |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Sch of Electronics, Elec Eng & Comp Sci |
| Organisation: |
Queen's University of Belfast |
| Scheme: |
Standard Research - NR1 |
| Starts: |
01 November 2017 |
Ends: |
30 April 2023 |
Value (£): |
765,827
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Computer Sys. & Architecture |
| Electronic Devices & Subsys. |
Fundamentals of Computing |
| Software Engineering |
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| EPSRC Industrial Sector Classifications: |
| Electronics |
Information Technologies |
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| Related Grants: |
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| Panel History: |
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Summary on Grant Application Form |
With the globalisation of supply chains the design and manufacture of today's electronic devices are now distributed worldwide, for example, through the use of overseas foundries, third party intellectual property (IP) and third party test facilities. Many different untrusted entities may be involved in the design and assembly phases and therefore, it is becoming increasingly difficult to ensure the integrity and authenticity of devices. The supply chain is now considered to be susceptible to a range of hardware-based threats, including hardware Trojans, IP piracy, integrated circuit (IC) overproduction or recycling, reverse engineering, IC cloning and side-channel attacks. These attacks are major security threats to military, medical, government, transportation, and other critical and embedded systems applications. The proposed project will use a common approach to investigate two of these threats, namely the use of deep-learning in the context of side-channel attacks and hardware Trojans.
Side-channel attacks (SCAs) exploit physical signal leakages, such as power consumption, electromagnetic emanations or timing characteristics, from cryptographic implementations, and have become a serious security concern with many practical real-world demonstrations, such as secret key recovery from the Mifare DESFire smart card used in public transport ticketing applications and from encrypted bitstreams on Xilinx Virtex-4/5 FPGAs. A hardware Trojan (HT) is a malicious modification of a circuit in order to control, modify, disable, monitor or affect the operation of the circuit. Although there have been no public reports of HTs detected in practice, in 2008 it was speculated that a critical failure in a Syrian radar may have been intentionally triggered via a hidden 'back door' inside a commercial off-the-shelf (COTS) microprocessor.
The proposed project seeks to investigate the application of deep learning in SCA and HT detection, with the ultimate goal of utilising deep learning based verification processes in Electronic Design Automation tools to provide feedback to designers on the security of their designs. In relation to the call, the project addresses the challenge of 'maintaining confidence in security through the development process', and more specifically 'building supply chain confidence' and 'novel hardware analysis toolsets and techniques'.
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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.qub.ac.uk |