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Neuromorphic Computing for Cognitive Augmentation in Cyber Defense

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Book cover Cybersecurity Systems for Human Cognition Augmentation

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

Abstract

The growth of digital content and information through the World Wide Web is increasing rapidly and more of this traffic is generated by smart mobile low size, weight, and power (SWaP) devices that are constantly sending/receiving information to/from the network for up-to-date operation. In terms of data, according to an IDC report by Gantz and Reinsel in 2012 [1], from 2005 to 2020, the digital universe will grow by a factor of 300, from 130 to 40,000 exabytes, and from now until 2020, the digital universe will about double every 2 years. The size of the digital universe in 2010 was estimated at 1,227 exabytes [1] in particular. Therefore, it can be expected that an increasing number of low SWaP devices will be implemented to offer enhanced functionality in terms of the complexity and number of services offered to users within the physically and electronically constrained form factor architecture. From a network security stand point, it will be important for the Army to ensure security and trust in the operation and functionality of smart mobile tactical devices. However, from the user’s point of view, performance degradation due to security add-ons may degrade device performance during operation and during operations where speed is critical, enhanced security could degrade operational effectiveness. Therefore, it is the main goal of this effort to perform basic research in methods and techniques to provide security to mobile tactical networks while ensuring low SWaP technical requirements for operation. In this pursuit, we have considered two basic research areas that could provide a revolutionary solution to the problem. The first technology area is memristor-based computing and the second area is artificial neural networks. It is expected that memristor-based physical computing architectures will deliver ultra-low SWaP and neural networks will enable parallelism and reconfiguration benefits. This chapter will provide a brief overview of the memristor technology and its applications within neural networks and their potential application to enabling human cognition augmentation in the Cyber-domain.

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Author information

Authors and Affiliations

  1. U.S. Department of Energy, Office of Science, Washington, DC, 20585, USA

    Robinson E. Pino

  2. Network Science Division, U.S. Army Research Laboratory, Adelphi, MD, 20783, USA

    Alexander Kott

Authors
  1. Robinson E. Pino
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  2. Alexander Kott
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Correspondence to Robinson E. Pino .

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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Pino, R.E., Kott, A. (2014). Neuromorphic Computing for Cognitive Augmentation in Cyber Defense. 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_2

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  • DOI: https://doi.org/10.1007/978-3-319-10374-7_2

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-10374-7

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