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Memristors as Synapses in Artificial Neural Networks: Biomimicry Beyond Weight Change

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Cybersecurity Systems for Human Cognition Augmentation

Part of the book series: Advances in Information Security ((ADIS,volume 61))

Abstract

Cyberthreat security is a rapidly evolving landscape, where the diversity and number of attacks is constantly changing, requiring new approaches to defense. In the past, it was sufficient to predict likely attack methods and to monitor potential vulnerabilities, however, today the attacks are too varied and change too quickly for the traditional defenses to be effective. We need structures that are capable of identifying probable attacks and responding without human intervention. Due to the increasing rate of new attack methodologies, these structures need to be able to identify and respond to attacks that have never been seen before. That is, we need structures which are capable of learning.

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Authors and Affiliations

  1. Cybersecurity Systems for Human Cognition Augmentation, Sandia National Laboratories, Albuquerque, New Mexico, 87185, USA

    Andrew J. Lohn, Patrick R. Mickel, James B. Aimone, Erik P. Debenedictis & Matthew J. Marinella

Authors
  1. Andrew J. Lohn
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  2. Patrick R. Mickel
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  3. James B. Aimone
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  4. Erik P. Debenedictis
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  5. Matthew J. Marinella
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Corresponding author

Correspondence to Andrew J. Lohn .

Editor information

Editors and Affiliations

  1. ICF International, Fairfax, Virginia, USA

    Robinson E. Pino

  2. Army Research Laboratory, Adelphi, Maryland, USA

    Alexander Kott

  3. ICF International, Darlington, Maryland, USA

    Michael Shevenell

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Lohn, A.J., Mickel, P.R., Aimone, J.B., Debenedictis, E.P., Marinella, M.J. (2014). Memristors as Synapses in Artificial Neural Networks: Biomimicry Beyond Weight Change. In: Pino, R., Kott, A., Shevenell, M. (eds) Cybersecurity Systems for Human Cognition Augmentation. Advances in Information Security, vol 61. Springer, Cham. https://doi.org/10.1007/978-3-319-10374-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-10374-7_9

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-10374-7

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