TY - GEN
T1 - Performance Comparison of Machine Learning Methods in DDoS Attack Detection in Smart Grids
AU - Meriaux, Edwin
AU - Koehler, David
AU - Islam, Md Zahidul
AU - Vokkarane, Vinod
AU - Lin, Yuzhang
N1 - Funding Information:
ACKNOWLEDGMENT This work relates to the Department of Navy award N00014-20-1-2858 and N00014-22-1-2001 issued by the Office of Naval Research. The United States Government has a royalty-free license throughout the world in all copyrightable material contained herein.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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, Naïve Bayes, and Extreme Gradient Boosting.
AB - 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, Naïve Bayes, and Extreme Gradient Boosting.
KW - CICIDS'17
KW - DDOS
KW - Decision Tree
KW - KDDCup'99
KW - Machine Learning
KW - Naïve Bayes
KW - Quadratic Discriminant Analysis
KW - Random Forest
KW - Smart Grids
KW - Support Vector Analysis
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85146702437&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146702437&partnerID=8YFLogxK
U2 - 10.1109/URTC56832.2022.10002244
DO - 10.1109/URTC56832.2022.10002244
M3 - Conference contribution
AN - SCOPUS:85146702437
T3 - 2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022
BT - 2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022
Y2 - 30 September 2022 through 2 October 2022
ER -