@inproceedings{4b118b5f1bdb4ad598724c96ccab55de,
title = "Performance Comparison of Machine Learning Methods in DDoS Attack Detection in Smart Grids",
abstract = "The integration of the cyber-network with the physical power grid makes it prone to cyber-Attacks disrupting the normal operation of the grid and therefore critical to detect. This paper compares how the detection of Distributed Denial of Service (DDOS) attacks, one of the most common types of cyber-Attack, on smart grids varies depending on the Machine Learning (ML) method used for detection, the different datasets used for the training, and the features of the dataset incorporated in the training. The most commonly used datasets namely KDDCup'99 and CICIDS'17 datasets are adapted for the sake of testing. The different ML methods used for these experiments are Decision Tree, Random Forest, Quadratic Discriminant Analysis, Support Vector Machine, Na{\"i}ve Bayes, and Extreme Gradient Boosting.",
keywords = "CICIDS'17, DDOS, Decision Tree, KDDCup'99, Machine Learning, Na{\"i}ve Bayes, Quadratic Discriminant Analysis, Random Forest, Smart Grids, Support Vector Analysis, XGBoost",
author = "Edwin Meriaux and David Koehler and Islam, {Md Zahidul} and Vinod Vokkarane and Yuzhang Lin",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022 ; Conference date: 30-09-2022 Through 02-10-2022",
year = "2022",
doi = "10.1109/URTC56832.2022.10002244",
language = "English (US)",
series = "2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022",
}