Detection of Deauthentication Threats in Wi-Fi Channels Using Machine Learning Strategies-Reference-Cited by-同舟云学术

Detection of Deauthentication Threats in Wi-Fi Channels Using Machine Learning Strategies

Published:2022-12-08 Issue: Volume: Page:
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Container-title:2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI)
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Author:

Latha R¹,R.M. Bommi²

Affiliation:

1. Saveetha Institute of Medical and Technical Science,Saveetha School of Engineering,Department of Computer Science and Engineering,Chennai,India

2. Saveetha Institute of Medical and Technical Science,Saveetha School of Engineering,Department of Electronics and Communication Engineering,Chennai,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10028778/10028782/10028874.pdf?arnumber=10028874

Reference11 articles.

1. Anomaly based intrusion detection for 802.11 networks with optimal features using SVM classifier

2. Detection of de-authentication dos attacks in wi-fi networks: A machine learning approach;mayank agarwal;IEEE International Workshop on InformationForensics and Security (WIFS),2015

3. Detection & analysis of evil twin attack in wireless network;modi;International Journal of Advanced Research in Computer Science,2017

4. A Lightweight Solution for Defending Against Deauthentication/Disassociation Attacks on 802.11 Networks

5. Detection and Prevention of De-authentication Attack in Real-time Scenario;VOLUME-8 ISSUE-10 AUGUST 2019 REGULAR ISSUE,2019

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