An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection
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 si...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/12/4398 |
| _version_ | 1827660497527242752 |
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| author | Konstantinos Demertzis Dimitrios Taketzis Vasiliki Demertzi Charalabos Skianis |
| author_facet | Konstantinos Demertzis Dimitrios Taketzis Vasiliki Demertzi Charalabos Skianis |
| author_sort | Konstantinos Demertzis |
| collection | DOAJ |
| description | 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 Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats.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. |
| first_indexed | 2024-03-09T23:52:31Z |
| format | Article |
| id | doaj.art-4b224dedb0bb4322ba2ed64f18bb08ac |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T23:52:31Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4b224dedb0bb4322ba2ed64f18bb08ac2023-11-23T16:30:30ZengMDPI AGEnergies1996-10732022-06-011512439810.3390/en15124398An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid ProtectionKonstantinos Demertzis0Dimitrios Taketzis1Vasiliki Demertzi2Charalabos Skianis3School of Science & Technology, Informatics Studies, Hellenic Open University, 26335 Patra, GreeceHellenic National Defence General Staff, Stratopedo Papagou, Mesogeion 227–231, 15561 Athens, GreeceDepartment of Computer Science, International Hellenic University, Kavala Campus, 57001 Thessaloniki, GreeceDepartment of Information and Communication Systems Engineering, University of Aegean, 83200 Karlovassi, GreeceThe 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.https://www.mdpi.com/1996-1073/15/12/4398smart energy gridscritical infrastructure protectionartificial immune systemIzhikevich spiking neural networksclonal selection algorithmtransfer learning |
| spellingShingle | Konstantinos Demertzis Dimitrios Taketzis Vasiliki Demertzi Charalabos Skianis An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection Energies smart energy grids critical infrastructure protection artificial immune system Izhikevich spiking neural networks clonal selection algorithm transfer learning |
| title | An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection |
| title_full | An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection |
| title_fullStr | An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection |
| title_full_unstemmed | An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection |
| title_short | An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection |
| title_sort | ensemble transfer learning spiking immune system for adaptive smart grid protection |
| topic | smart energy grids critical infrastructure protection artificial immune system Izhikevich spiking neural networks clonal selection algorithm transfer learning |
| url | https://www.mdpi.com/1996-1073/15/12/4398 |
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