Detection and Categorization of Conflict Flows Within SDN Environments using Machine Learning Approach-Reference-Cited by-同舟云学术

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Detection and Categorization of Conflict Flows Within SDN Environments using Machine Learning Approach

Published:2024-02-22 Issue: Volume:22 Page:1-6
ISSN:
Container-title:2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)
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Author:

S Kousalya¹,Sundararajan Kanthimathi²,Vasudevan I. Jaichitra³,Sheela L. Jaba³,Thyagarajan Sarunya²

Affiliation:

1. Panimalar Engineering College,M.E. Computer Science and Engineering,Chennai,Tamil Nadu,India

2. Panimalar Engineering College,B.E. Computer Science and Engineering,Chennai,Tamil Nadu,India

3. Panimalar Engineering college,Dept of Computer Science and Engineering,Chennai,Tamil Nadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10493200/10493145/10493381.pdf?arnumber=10493381

Reference30 articles.

1. Detecting Saturation Attacks in SDN via Machine Learning

2. Cyberpulse: A Machine Learning Based Link Flooding Attack Mitigation System for Software Defined Networks

3. An Efficient SDN-Based DDoS Attack Detection and Rapid Response Platform in Vehicular Networks

4. Load Balancing Mechanisms in the Software Defined Networks: A Systematic and Comprehensive Review of the Literature

5. DETPro: A High-Efficiency and Low-Latency System Against DDoS Attacks in SDN Based on Decision Tree

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