A Machine Learning Classification Model Using Random Forest for Detecting DDoS Attacks-Reference-Cited by-同舟云学术

A Machine Learning Classification Model Using Random Forest for Detecting DDoS Attacks

Published:2022-07-19 Issue: Volume: Page:
ISSN:
Container-title:2022 International Symposium on Networks, Computers and Communications (ISNCC)
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Author:

Chu Thai Son¹,Si Weisheng¹,Simoff Simeon²,Nguyen Quang Vinh²

Affiliation:

1. Western Sydney University,School of Computer, Data & Mathematical Sciences,Sydney,NSW,Australia

2. MARCS Institute and School of Computer, Data & Mathematical Sciences Western Sydney University,Sydney,NSW,Australia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9851698/9851699/09851797.pdf?arnumber=9851797

Reference30 articles.

1. Identification of Disturbances in Power System and DDoS Attacks using Machine Learning

2. DDoSNet: A Deep-Learning Model for Detecting Network Attacks

3. Use of machine learning to analyse intrusion detection performance;kiourkoulis;Inf Secur,2020

4. A Cooperative and Hybrid Network Intrusion Detection Framework in Cloud Computing Based on Snort and Optimized Back Propagation Neural Network

5. Network Intrusion Detection for Distributed Denial-of-Service (DDoS) Attacks using Machine Learning Classification Techniques by Yasar Shahid Hussain;Honours Bachelor Sci,2020

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1. Multiclassification Analysis of Volumetric, Protocol, and Application Layer DDoS Attacks;Lecture Notes on Data Engineering and Communications Technologies;2024

2. A Two-level DDoS attack Detection using Entropy and Machine Learning in SDN;2023 3rd International Conference on Intelligent Technologies (CONIT);2023-06-23