Instance Selection with Naïve Bayes to Improve DDoS Attack Classification Accuracy Using Random Forest

DOI

10.2991/978-94-6463-480-8_19How to use a DOI?

Keywords

Classification; DDoS; Random Forest; Naïve Bayes

Abstract

DDoS Attack is one of the threats in a series of network systems. Attacks on a network in one unit of time can subsequently occur in a very large number of attacks. Previous research has been done to avoid DDoS attack through classification process and one of which is based on Random Forest method. The large number of attacks requires classification. In previous research, Random Forest was one way to classify DDoS attacks. The classification used is using the Random Forest algorithm. The Random Forest classification model produces an accuracy of 98.02%. This research is a preprocessing step involving Naïve Bayes instance selection which is compared with Adaboost instance selection which is expected to remove noise data due to the relatively large amount of data. With large quantities, it is hoped that this preprocessing step can get maximum results. The research also involved the Naïve Bayes and ZeroR classification methods, where the best results were using Naïve Bayes instance selection with the random forest classification method with an accuracy of 100%.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Aditya Putra Ramdani
AU  - Achmad Solichan
AU  - Muhammad Zainudin Al Amin
AU  - Nova Christina Sari
AU  - Basirudin Ansor
AU  - Mulil Khaira
PY  - 2024
DA  - 2024/07/29
TI  - Instance Selection with Naïve Bayes to Improve DDoS Attack Classification Accuracy Using Random Forest
BT  - Proceedings of the 2nd Lawang Sewu Internasional Symposium on Engineering and Applied Sciences (LEWIS-EAS 2023)
PB  - Atlantis Press
SP  - 232
EP  - 240
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-480-8_19
DO  - 10.2991/978-94-6463-480-8_19
ID  - Ramdani2024
ER  -