Deep Learning with Dense Random Neural Networks for Detecting Attacks Against IoT-Connected Home Environments-Reference-Cited by-同舟云学术

Deep Learning with Dense Random Neural Networks for Detecting Attacks Against IoT-Connected Home Environments

Published:2018 Issue: Volume: Page:79-89
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Brun Olivier,Yin Yonghua,Gelenbe Erol,Kadioglu Y. Murat,Augusto-Gonzalez Javier,Ramos Manuel

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-95189-8_8

Reference19 articles.

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2. Dalrymple, S.D.: Comparison of ZigBee replay attacks using a universal software radio peripheral and USB radio. Master’s thesis, AFIT, USAF (2014)

3. Lecture Notes in Computer Science;A Mauro Di,2013

4. Dubey, A., Jain, V., Kumar, A.: A survey in energy drain attacks and their countermeasures in wireless sensor networks. Int. J. Eng. Res. Technol. 3(2), 1206–1210 (2014)

5. Falk, R., Hof, H.-J.: Fighting insomnia: a secure wake-up scheme for wireless sensor networks. In: 3rd International Conference on Emerging Security Information, Systems and Technologies, IEEE SECURWARE 2009, pp. 191–196 (2009)

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