Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Mittal, Sudip
Committee Member
Swan, J. Edward, II
Committee Member
Torri, Stephen
Date of Degree
5-12-2023
Document Type
Graduate Thesis - Open Access
Major
Computer Science
Degree Name
Master of Science (M.S.)
College
James Worth Bagley College of Engineering
Department
Department of Computer Science and Engineering
Abstract
In this thesis, different Machine learning (ML) algorithms were used in the detection of anomalies using a dataset from a Gas pipeline SCADA system which was generated by Mississippi State University’s SCADA laboratory. This work was divided into two folds: Binary Classification and Categorized classification. In the binary classification, two attack types namely: Command injection and Response injection attacks were considered. Eight Machine Learning Classifiers were used and the results were compared. The Light GBM and Decision tree classifiers performed better than the other algorithms. In the categorical classification task, Seven (7) attack types in the dataset were analyzed using six different ML classifiers. The light gradient-boosting machine (LGBM) outperformed all the other classifiers in the detection of all the attack types. One other aspect of the categorized classification was the use of an autoencoder in improving the performance of all the classifiers used. The last part of this thesis was using SHAP plots to explain the features that accounted for each attack type in the dataset.
Recommended Citation
Fiah, Eric Kudjoe, "Anomaly detection in SCADA systems using machine learning" (2023). Theses and Dissertations. 5824.
https://scholarsjunction.msstate.edu/td/5824