Enabling Sustainable Cyber Physical Security Systems through Neuromorphic Computing

Li, Jialing; Liu, Lingjia; Zhao, Chao; Hamedani, Kian; Atat, Rachad; Yi, Yang

doi:10.1109/tsusc.2017.2717807

Cited by 16 publications

(6 citation statements)

References 34 publications

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“…There have been some works on the verification problem of CPSs. In Reference 14, Li et al combine CPSs with machine learning and propose an approach for detecting anomalies based on reservoir computing. In this approach, sustainable computing is achieved by maintaining lower power consumption and high performance.…”

Section: Related Workmentioning

confidence: 99%

Compatibility checking for cyber‐physical systems based on microservices

Dai

et al. 2022

Softw Pract Exp

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Microservices architecture provides a promising solution for developing sustainable cyber-physical systems (CPSs). However, checking the compatibility of CPSs over a set of microservices communicating asynchronously via unbounded buffers are undecidable due to their infinite state spaces. In this article, we propose a new approach for checking the compatibility of CPSs with infinite state spaces without restricting the size of buffers or the number of communication cycles. First, we integrate CPSs with microservice architecture and design the system architecture for building CPSs over a set of cyber-physical microservices with unbounded buffers. Second, we model CPSs composed of asynchronously communicating cyber-physical microservices via FIFO buffers as labelled transition systems. Third, we adopt the stability notion and present a sufficient condition for checking the unspecified receptions of CPSs through stability checking. Finally, we implement our approach in Process Analysis Toolkit for automatic compatibility checking and conduct experiments to show our approach is effective and efficient.

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Section: Related Workmentioning

confidence: 99%

Compatibility checking for cyber‐physical systems based on microservices

Dai

et al. 2022

Softw Pract Exp

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“…Compared to conventional neural networks, spiking neural networks are more analogous to human brains and consume much less power. Recently, neuromorphic computing has been successfully applied to many applications [54][55][56][57][58][59].…”

Section: Neuromorphic Computingmentioning

confidence: 99%

Quantized Neural Networks and Neuromorphic Computing for Embedded Systems

Liu¹,

Yi²

2020

Intelligent System and Computing

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Deep learning techniques have made great success in areas such as computer vision, speech recognition and natural language processing. Those breakthroughs made by deep learning techniques are changing every aspect of our lives. However, deep learning techniques have not realized their full potential in embedded systems such as mobiles, vehicles etc. because the high performance of deep learning techniques comes at the cost of high computation resource and energy consumption. Therefore, it is very challenging to deploy deep learning models in embedded systems because such systems have very limited computation resources and power constraints. Extensive research on deploying deep learning techniques in embedded systems has been conducted and considerable progress has been made. In this book chapter, we are going to introduce two approaches. The first approach is model compression, which is one of the very popular approaches proposed in recent years. Another approach is neuromorphic computing, which is a novel computing system that mimicks the human brain.

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“…As a result, both power consumption and delays can be optimised. Related to security, Li et al [20] developed a novel architecture based on analogue spiking reservoir computing, which can be applied to anomaly detection in smart grids. The solution exhibits performance that is comparable to other state-of-the-art proposals, however, it is more energy-efficient, which makes the work a contribution to sustainable computing.…”

Section: A Green and Sustainable Computingmentioning

confidence: 99%

Sustainable Security for Internet of Things

Donno

Malarski

Fafoutis

et al. 2019

2019 International Conference on Smart Applications, Communications and Networking (SmartNets)

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Internet of Things (IoT) has been one of the leading innovations of the 21 st century. However, the future of the IoT heavily depends on the ability of current technologies to incorporate it. On one side, end-devices and network communication need to be energy efficient, affordable, and environmentally friendly. On the other side, no IoT deployment should be placed without ensuring seamless system and network security. In this paper, we define Sustainable Security for Internet of Things (SSIoT), a new research angle that aims at addressing the challenges posed by the IoT, placing sustainability and security as its key pillars.

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Enabling Sustainable Cyber Physical Security Systems through Neuromorphic Computing

Cited by 16 publications

References 34 publications

Compatibility checking for cyber‐physical systems based on microservices

Compatibility checking for cyber‐physical systems based on microservices

Quantized Neural Networks and Neuromorphic Computing for Embedded Systems

Sustainable Security for Internet of Things

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