Fault Detection and Isolation Based on Artificial Immune System for Injection Molding Machine

Ren, Yanheng; Wang, Xianghua; Zhang, Chunming; Gao, Yuxia

doi:10.1109/cac51589.2020.9327891

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…The need to upgrade energy infrastructure with the integration of new technologies was quickly realized by various digital security agencies [8,9], who were active in research to protect the infrastructure in question [10,11]. For example, Naruchitparames et al [12] presented a model for ensuring the privacy and integrity of communication parts within an intelligent network in which smart meters are widely used, which in this architecture are used as communication gateways within and between energy infrastructures.…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection

et al. 2022

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The rate of technical innovation, system interconnection, and advanced communications undoubtedly boost distributed energy networks’ efficiency. However, when an additional attack surface is made available, the possibility of an increase in attacks is an unavoidable result. The energy ecosystem’s significant variety draws attackers with various goals, making any critical infrastructure a threat, regardless of scale. Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats. As a result, robust artificial intelligence cyber-defense solutions are more important than ever. Based on the above challenge, this paper proposes an ensemble transfer learning spiking immune system for adaptive smart grid protection. It is an innovative Artificial Immune System (AIS) that uses a swarm of Evolving Izhikevich Neural Networks (EINN) in an Ensemble architecture, which optimally integrates Transfer Learning methodologies. The effectiveness of the proposed innovative system is demonstrated experimentally in multiple complex scenarios that optimally simulate the modern energy environment. The most significant findings of this work are that the transfer learning architecture’s shared learning rate significantly adds to the speed of generalization and convergence approach. In addition, the ensemble combination improves the accuracy of the model because the overall behavior of the numerous models is less noisy than a comparable individual single model. Finally, the Izhikevich Spiking Neural Network used here, due to its dynamic configuration, can reproduce different spikes and triggering behaviors of neurons, which models precisely the problem of digital security of energy infrastructures, as proved experimentally.

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