An Unsupervised Attack Detection Approach for Software Defined Networks-Reference-Cited by-同舟云学术

An Unsupervised Attack Detection Approach for Software Defined Networks

Published:2022-11-24 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)
language:
Short-container-title:

Author:

Chouhan Ravindra Kumar¹,Atulkar Mithilesh¹,Nagwani Naresh Kumar²

Affiliation:

1. National Institute of Technology Raipur,Department of Computer Applications,Raipur,India

2. National Institute of Technology Raipur,Dept of Computer Sc. & Engg.,Raipur,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10010702/10010572/10010577.pdf?arnumber=10010577

Reference22 articles.

1. A framework to detect DDoS attack in Ryu controller based software defined networks using feature extraction and classification

2. Discriminative multinomial naive bayes for network intrusion detection;panda;2010 6th International Conference on Information Assurance and Security IAS 2010,0

3. An Enhanced Resilient Backpropagation Artificial Neural Network for Intrusion Detection System;naoum;IJCSNS International Journal of Computer Science and Network Security,2013

4. An analysis of supervised tree based classifiers for intrusion detection system

5. Unsupervised Clustering Approach for Network Anomaly Detection

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Detecting Anomalies in the Optical Layer Using Unsupervised Machine Learning;Optical Fiber Communication Conference (OFC) 2024;2024

2. An Approach to Attack Detection on Graph-Based Modeling of States in Critical Infrastructures;2023 International Russian Automation Conference (RusAutoCon);2023-09-10