Fault-Tolerant Learning in Spiking Astrocyte-Neural Networks on FPGAs

Johnson, Anju P.; Liu, Junxiu; Millard, Alan G.; Karim, Shvan; Tyrrell, Andy M.; Harkin, Jim; Timmis, Jon; McDaid, Liam; Halliday, David M.

doi:10.1109/vlsid.2018.36

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…For real-world applications, this parallelism can be exploited to execute tasks orders of magnitude faster than in software. One aspect of the proposed model is that it operates at an accelerated biological timescale; similar concepts are illustrated in our previous works [40,41].…”

Section: Hardware Results On Xilinx Artix-7 Fpgamentioning

confidence: 75%

Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

Johnson

Al-Aqrabi

Hill

2020

Sensors

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Internet of Things (IoT) and Cyber-Physical Systems (CPS) have profoundly influenced the way individuals and enterprises interact with the world. Although attacks on IoT devices are becoming more commonplace, security metrics often focus on software, network, and cloud security. For CPS systems employed in IoT applications, the implementation of hardware security is crucial. The identity of electronic circuits measured in terms of device parameters serves as a fingerprint. Estimating the parameters of this fingerprint assists the identification and prevention of Trojan attacks in a CPS. We demonstrate a bio-inspired approach for hardware Trojan detection using unsupervised learning methods. The bio-inspired principles of pattern identification use a Spiking Neural Network (SNN), and glial cells form the basis of this work. When hardware device parameters are in an acceptable range, the design produces a stable firing pattern. When unbalanced, the firing rate reduces to zero, indicating the presence of a Trojan. This network is tunable to accommodate natural variations in device parameters and to avoid false triggering of Trojan alerts. The tolerance is tuned using bio-inspired principles for various security requirements, such as forming high-alert systems for safety-critical missions. The Trojan detection circuit is resilient to a range of faults and attacks, both intentional and unintentional. Also, we devise a design-for-trust architecture by developing a bio-inspired device-locking mechanism. The proposed architecture is implemented on a Xilinx Artix-7 Field Programmable Gate Array (FPGA) device. Results demonstrate the suitability of the proposal for resource-constrained environments with minimal hardware and power dissipation profiles. The design is tested with a wide range of device parameters to demonstrate the effectiveness of Trojan detection. This work serves as a new approach to enable secure CPSs and to employ bio-inspired unsupervised machine intelligence.

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Section: Hardware Results On Xilinx Artix-7 Fpgamentioning

confidence: 75%

Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

Johnson

Al-Aqrabi

Hill

2020

Sensors

Self Cite

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“…The output layer neuron provides a stable enable signal for the client FPGA if the received device parameters are within scope. This principle of using a spiking neural network is derived from [32,33], and hardware realization of the approach is described in [34].…”

Section: Device Parameter Analysis Of Client Fpgasmentioning

confidence: 99%

“…However, in [32][33][34], the authors derive bio-inspired principles for homeostasis targeting robotic applications, where this paper emphasis the use of similar methodologies for hardware Trojan detection. Bio-inspired computing develops computational models using various models of biology.…”

Section: Device Parameter Analysis Of Client Fpgasmentioning

confidence: 99%

Hardware-Intrinsic Multi-Layer Security: A New Frontier for 5G Enabled IIoT

Al-Aqrabi

Johnson

Hill

et al. 2020

Sensors

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The introduction of 5G communication capabilities presents additional challenges for the development of products and services that can fully exploit the opportunities offered by high bandwidth, low latency networking. This is particularly relevant to an emerging interest in the Industrial Internet of Things (IIoT), which is a foundation stone of recent technological revolutions such as Digital Manufacturing. A crucial aspect of this is to securely authenticate complex transactions between IIoT devices, whilst marshalling adversarial requests for system authorisation, without the need for a centralised authentication mechanism which cannot scale to the size needed. In this article we combine Physically Unclonable Function (PUF) hardware (using Field Programmable Gate Arrays—FPGAs), together with a multi-layer approach to cloud computing from the National Institute of Standards and Technology (NIST). Through this, we demonstrate an approach to facilitate the development of improved multi-layer authentication mechanisms. We extend prior work to utilise hardware security primitives for adversarial trojan detection, which is inspired by a biological approach to parameter analysis. This approach is an effective demonstration of attack prevention, both from internal and external adversaries. The security is further hardened through observation of the device parameters of connected IIoT equipment. We demonstrate that the proposed architecture can service a significantly high load of device authentication requests using a multi-layer architecture in an arbitrarily acceptable time of less than 1 second.

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A Reliability Assessment Approach for A LIF Neurons Based Spiking Neural Network Circuit

Sun

Xie

2023

2023 24th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and

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Fault-Tolerant Learning in Spiking Astrocyte-Neural Networks on FPGAs

Cited by 5 publications

References 22 publications

Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

Hardware-Intrinsic Multi-Layer Security: A New Frontier for 5G Enabled IIoT

A Reliability Assessment Approach for A LIF Neurons Based Spiking Neural Network Circuit

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