Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 3 June 2022 / Approved: 6 June 2022 / Online: 6 June 2022 (09:14:03 CEST)

A peer-reviewed article of this Preprint also exists.

Demertzis, K.; Taketzis, D.; Demertzi, V.; Skianis, C. An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection. Energies 2022, 15, 4398. Demertzis, K.; Taketzis, D.; Demertzi, V.; Skianis, C. An Ensemble Transfer Learning Spiking Immune System for Adaptive Smart Grid Protection. Energies 2022, 15, 4398.

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 Outdated technology and other antiquated countermeasures that worked years ago cannot address the complexity of current threats.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. In this way, the proposed system fully automates the strategic security planning of energy networks with computational intelligence methods. It allows the complete control of the digital strategies of the potential infrastructure that frames it, thus contributing to the timely and valid decision-making during cyber-attacks.

Keywords

Smart Energy Grids; Critical Infrastructure Protection; Artificial Immune System; Izhikevich Spiking Neural Networks; Clonal Selection Algorithm; Transfer Learning; Ensemble Learning

Subject

Engineering, Electrical and Electronic Engineering

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