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Intelligent Situational-Awareness Architecture for Hybrid Emergency Power Systems in More Electric Aircraft

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Deep Learning Applications for Cyber Security

Abstract

In this chapter, we exploit the deep learning and adaptive neuro-fuzzy inference system (ANFIS) techniques to develop an intelligent situational awareness system for energy management systems of the emergency hybrid auxiliary power unit (APU) for more-electric aircrafts. Our proposed security control strategy consists of two main mechanisms: (1) deep learning-based attack detection scheme that explores the techniques of convolutional neural networks, deconvolutional neural networks, and recurrent neural networks and (2) adaptive neuro-fuzzy inference system (ANFIS)-based estimation method to calculate the true values of the compromised data. In this chapter, we also present some simulation results to illustrate the effectiveness of our proposed method in detecting the cyber-attacks, such as false data injection (FDI) attacks, and mitigating the impact of the cyber-attacks in the energy management for the hybrid APUs in more-electric aircrafts.

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Authors and Affiliations

  1. The University of Akron, Akron, OH, USA

    Gihan J. Mendis, Mohasinina Binte Kamal & Jin Wei

Authors
  1. Gihan J. Mendis
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  2. Mohasinina Binte Kamal
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  3. Jin Wei
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Correspondence to Gihan J. Mendis .

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Editors and Affiliations

  1. Charles Darwin University, Casuarina, NT, Australia

    Mamoun Alazab

  2. Singtel Optus, Sydney, NSW, Australia

    MingJian Tang

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Mendis, G.J., Kamal, M.B., Wei, J. (2019). Intelligent Situational-Awareness Architecture for Hybrid Emergency Power Systems in More Electric Aircraft. In: Alazab, M., Tang, M. (eds) Deep Learning Applications for Cyber Security. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-13057-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-13057-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13056-5

  • Online ISBN: 978-3-030-13057-2

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