The limitations of unsupervised machine learning for identifying malicious nodes in IoT networks
Author:
Affiliation:
1. IRIMAS Institute USTOMB university, University of Haute Alsace,SIMPA Laboratory,Algeria,France
2. IRIMAS Institute University of Haute,Alsace,France
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10000063/10000593/10001314.pdf?arnumber=10001314
Reference22 articles.
1. Misbehavior detection in the Internet of Things: A network-coding-aware statistical approach
2. Modeling the Greedy Behavior Attack and Analyzing its Impact on IoT Networks
3. Detection of Greedy Behavior in WSN Using IEEE 802.15 Protocol
4. A Dynamic Game with Adaptive Strategies for IEEE 802.15.4 and IoT
5. ProFiOt: Abnormal Behavior Profiling (ABP) of IoT devices based on a machine learning approach
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