A Novel Deep Learning-based Framework for Blackhole Attack Detection in Unsecured RPL Networks
Author:
Affiliation:
1. Moulay Ismail University of Meknes,IMAGE Laboratory, Faculty of Sciences,Meknes,Morocco
2. Uae University,College of Information Technology,Al Ain,UAE
3. Moulay Ismail University of Meknes,IMAGE Laboratory, School of Technology,Meknes,Morocco
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9989532/9990057/09990664.pdf?arnumber=9990664
Reference19 articles.
1. Abnormal Network Traffic Detection using Deep Learning Models in IoT environment
2. Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)
3. Adversarial training for deep learning-based cyberattack detection in IoT-based smart city applications
4. RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning
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1. Towards a Lightweight Detection System Leveraging Ranking Techniques with Wrapper Feature Selection Algorithm for Selective Forwarding Attacks in Low power and Lossy Networks of IoTs;2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA);2024-08-06
2. Hybrid Intrusion Detection System for RPL IoT Networks Using Machine Learning and Deep Learning;IEEE Access;2024
3. Intrusion Detection Model for IoT Networks Using Graph Convolution Networks(GCN);ICT for Intelligent Systems;2023
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