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Erschienen in:
Situational Awareness, Sensemaking, and Situation Understanding in Cyber Warfare Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

10. Low Power Neuromorphic Architectures to Enable Pervasive Deployment of Intrusion Detection Systems

verfasst von : Tarek M. Taha, Raqibul Hasan, Chris Yakopcic, Mark R. McLean

Erschienen in: Cybersecurity Systems for Human Cognition Augmentation

Verlag: Springer International Publishing

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Abstract

Intrusion detection systems (IDS) are commonly utilized to prevent cyber-attacks. With the wide proliferation of network connected devices, running IDS algorithms on all devices (including mobile devices) within a network can help bolster security. However, the cost of running IDS algorithms on all networked devices can be high in terms of power and physical resources (especially battery operated ones). Several recent studies have proposed mapping applications to neural network form and then running these on specialized neural network accelerators [1, 2] to reduce power consumption. Neural accelerators can result in power reduction from about 2 times to several thousand times compared to RISC processors [3]. Hence utilizing these neural network accelerators can enabling the deployment of IDS algorithms across all devices in a network.

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Vorheriges Kapitel Memristors as Synapses in Artificial Neural Networks: Biomimicry Beyond Weight Change
Nächstes Kapitel Memristor SPICE Model Simulation and Device Hardware Correlation
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Metadaten
Titel
Low Power Neuromorphic Architectures to Enable Pervasive Deployment of Intrusion Detection Systems
verfasst von
Tarek M. Taha
Raqibul Hasan
Chris Yakopcic
Mark R. McLean
Copyright-Jahr
2014
Buch
Cybersecurity Systems for Human Cognition Augmentation

Print ISBN: 978-3-319-10373-0

Electronic ISBN: 978-3-319-10374-7

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-10374-7_10