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Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments

  • Published:2018 Issue: Volume:134 Page:458-463
  • ISSN:1877-0509
  • Container-title:Procedia Computer Science
  • language:en
  • Short-container-title:Procedia Computer Science

Author:

Brun Olivier,Yin Yonghua,Gelenbe Erol

Publisher

Elsevier BV

Subject

General Engineering

Reference19 articles.

1. R. Rehim. Python Penetration Testing Cookbook Packt Publishing, 2017.

2. Counter Hack Reloaded: A Step-by-Step Guide to Computer Attacks and Effective Defenses;Skoudis,2005

3. Wireless sensor network denial of sleep attack;Brownfield,2005

4. S. D. Dalrymple. Comparison of zigbee replay attacks using a universal software radio peripheral and usb radio. Master’s thesis, AFIT, USAF, 2014.

5. Detecting and Preventing Beacon Replay Attacks in Receiver-Initiated MAC Protocols for Energy Efficient WSNs;Di Mauro,2013

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3. Genetic Algorithm based hyperparameter tuned CNN for identifying IoT intrusions;KSII Transactions on Internet and Information Systems;2024-03-31

4. Online Self-Supervised Deep Learning for Intrusion Detection Systems;IEEE Transactions on Information Forensics and Security;2024

5. Review of artificial intelligence for enhancing intrusion detection in the internet of things;Engineering Applications of Artificial Intelligence;2024-01
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