HCTDDA: Hybrid Classification Technique for Detection of DDoS Attacks-Reference-Cited by-同舟云学术

HCTDDA: Hybrid Classification Technique for Detection of DDoS Attacks

Published:2021-10-22 Issue: Volume: Page:
ISSN:
Container-title:2021 5th International Conference on Information Systems and Computer Networks (ISCON)
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Author:

Gaur Vimal¹^ORCID,Kumar Rajneesh¹^ORCID

Affiliation:

1. Maharishi Markandeshwar (Deemed to be University), Mullana,CSE Dept. MMEC,Ambala,India,133207

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9702150/9702296/09702399.pdf?arnumber=9702399

Reference24 articles.

1. Detection of known and unknown DDoS attacks using Artificial Neural Networks

2. A Neural-Network Based DDoS Detection System Using Hadoop and HBase

3. Detecting distributed denial of service attacks using data mining techniques;alkasassbeh;International Journal of Advanced Computer Science and Applications (IJACSA),2016

4. Detection DDoS attacks based on neural-network using Apache Spark

5. Attack Detection of Distributed Denial of Service Based on Splunk;su;Proceedings of the IEEE International Conference on Advanced Materials for Science and Engineering,2017

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3. ET-RF based Model for Detection of Distributed Denial of Service Attacks;2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS);2022-04-07

4. FSMDAD: Feature Selection Method for DDoS Attack Detection;2022 International Conference on Electronics and Renewable Systems (ICEARS);2022-03-16

5. DDoSLSTM: Detection of Distributed Denial of Service Attacks on IoT Devices using LSTM Model;2022 International Conference on Communication, Computing and Internet of Things (IC3IoT);2022-03-10