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

Konstantinos Demertzis, Dimitrios Taketzis, Vasiliki Demertzi and Charalabos Skianis
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Konstantinos Demertzis: School of Science & Technology, Informatics Studies, Hellenic Open University, 26335 Patra, Greece
Dimitrios Taketzis: Hellenic National Defence General Staff, Stratopedo Papagou, Mesogeion 227–231, 15561 Athens, Greece
Vasiliki Demertzi: Department of Computer Science, International Hellenic University, Kavala Campus, 57001 Thessaloniki, Greece
Charalabos Skianis: Department of Information and Communication Systems Engineering, University of Aegean, 83200 Karlovassi, Greece

Energies, 2022, vol. 15, issue 12, 1-19

Abstract: 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 Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats.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.

Keywords: smart energy grids; critical infrastructure protection; artificial immune system; Izhikevich spiking neural networks; clonal selection algorithm; transfer learning; ensemble learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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