Machine learning approach detects DDoS attacks

Authors

  • Doan Trung Son
  • Nguyen Thi Khanh Tram
  • Tran Thi Thu

DOI:

https://doi.org/10.54654/isj.v1i15.850

Keywords:

DDoSt, KNN, Decision Tree, Random Forest, SVM

Tóm tắt

Abstract— Denial of Service attacks have been around since the dawn of the internet age. Along with the development and explosion of the Internet, denial of service attacks are also increasingly powerful and become a serious threat in cyberspace. The article aims to evaluate machine learning algorithms: K-nearest neighbor (KNN) algorithm, Decision Tree, Random Forest algorithm and Support Vector Machine (SVM) on various metrics in detecting DDoS attacks. The main objective of the paper is to analyze the algorithms, collect data and evaluate the effectiveness of the algorithms in DDoS attack detection.

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References

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Abstract views: 239 / PDF downloads: 112

Published

2022-06-08

How to Cite

Son, D. T., Tram, N. T. K., & Thu, T. T. . (2022). Machine learning approach detects DDoS attacks. Journal of Science and Technology on Information Security, 1(15), 102-108. https://doi.org/10.54654/isj.v1i15.850

Issue

No 1. CS (15) 2022

Section

Papers

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3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

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