Machine Learning-based Anomaly Detection in ZigBee Networks-Reference-Cited by-同舟云学术

Machine Learning-based Anomaly Detection in ZigBee Networks

Published:2022-11-14 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Computing (ICOCO)
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Author:

Oshio Tomoya¹,Okada Satoshi²,Mitsunaga Takuho³

Affiliation:

1. Toyo University,Tokyo,Japan

2. The University of Tokyo,Tokyo,Japan

3. Toyo University The Tokyo Foundation for Policy Research,INIAD,Tokyo,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10031269/10031273/10031837.pdf?arnumber=10031837

Reference20 articles.

1. New LDoS Attack in Zigbee Network and its Possible Countermeasures

2. A Novel Low-Rate Denial of Service Attack Detection Approach in ZigBee Wireless Sensor Network by Combining Hilbert-Huang Transformation and Trust Evaluation

3. A zigbee intrusion detection system for iot using secure and efficient data collection;sadikin;Internet of the Things,2020

4. Ldos attacker detection algorithms in zigbee network;okada;2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) Melbourne Australia December 6-8 2021,2021

5. 3.18. the “packet list” pane,2022

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