Machine Learning Based Intrusion Detection Systems for IoT Applications-Reference-Cited by-同舟云学术

Machine Learning Based Intrusion Detection Systems for IoT Applications

Published:2019-11-30 Issue:4 Volume:111 Page:2287-2310
ISSN:0929-6212
Container-title:Wireless Personal Communications
language:en
Short-container-title:Wireless Pers Commun

Author:

Verma Abhishek^ORCID,Ranga Virender

Funder

Ministry of Human Resource Development

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Link

http://link.springer.com/content/pdf/10.1007/s11277-019-06986-8.pdf

Reference72 articles.

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2. (2017). CIDDS-001 dataset. Retrieved November 3, 2019, from https://www.hs-coburg.de/forschung-kooperation/forschungsprojekte-oeffentlich/ingenieurwissenschaften/cidds-coburg-intrusion-detection-data-sets.html.

3. (2017). NSL-KDD dataset. Retrieved November 3, 2019, from http://nsl.cs.unb.ca/nsl-kdd/.

4. (2017). UNSW-NB15 dataset. Retrieved November 3, 2019, from https://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets/.

5. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys Tutorials, 17(4), 2347–2376.

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