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.

<p> The work of this study is divided into two parts. Part I reports on the development of a dedicated minicomputer, data acquisition, and reduction system for GPC. The hardware, software, and operating performance of the system is discussed in some detail.</p> <p> Part II, reports on an experimental study design to determine whether axial dispersion corrections are universal in the sense of being independent of polymer composition. Results for poly(vinyl-chloride), polystyrene, polybutadiene, and poly(methy-methacrylate) are discussed.</p> / Thesis / Master of Engineering (MEngr)

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