The Repository @ St. Cloud State

Open Access Knowledge and Scholarship

Date of Award


Culminating Project Type




Degree Name

Information Assurance: M.S.


Information Assurance and Information Systems


Herberger School of Business

First Advisor

Dr. Jim Q. Chen

Second Advisor

Dr. Shakour A. Abuzneid

Third Advisor

Dr. Akalanka B. Mailewa

Fourth Advisor

Dr. Abdullah Abu Hussein

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Keywords and Subject Headings

Intrusion Detection Systems (IDS), Machine Learning, Deep Learning, Principal Component Analysis (PCA), Support Vector Machine (SVM), Autoencoder Neural Network (ANN), NSL-KDD Dataset


Advancements in computing technology have created additional network attack surface, allowed the development of new attack types, and increased the impact caused by an attack. Researchers agree, current intrusion detection systems (IDSs) are not able to adapt to detect these new attack forms, so alternative IDS methods have been proposed. Among these methods are machine learning-based intrusion detection systems. This research explores the current relevant studies related to intrusion detection systems and machine learning models and proposes a new hybrid machine learning IDS model consisting of the Principal Component Analysis (PCA) and Support Vector Machine (SVM) learning algorithms. The NSL-KDD Dataset, benchmark dataset for IDSs, is used for comparing the models’ performance. The performance accuracy and false-positive rate of the hybrid model are compared to the results of the model’s individual algorithmic components to determine which components most impact attack prediction performance. The performance metrics of the hybrid model are also compared to two deep learning Autoencoder Neuro Network models and the results found that the complexity of the model does not add to the performance accuracy. The research showed that pre-processing and feature selection impact the predictive accuracy across models. Future research recommendations were to implement the proposed hybrid IDS model into a live network for testing and analysis, and to focus research into the pre-processing algorithms that improve performance accuracy, and lower false-positive rate. This research indicated that pre-processing and feature selection/feature extraction can increase model performance accuracy and decrease false-positive rate helping businesses to improve network security.


Thank you to my Thesis Chair, Dr. Jim Chen for sharing your knowledge and guiding me through this process. Your encouragement was always appreciated. Thank you to Dr. Abu Hussein for helping me at the initial stage of finding my research interests and selecting a topic. Thank you to Dr. Akalanka Mailewa for providing me resources and helping me to narrow down my research topic to intrusion detection and machine learning. Thank you to Dr. Abuzneid for your encouragement and guidance with machine learning and python code. I am blessed to have had such an encouraging and knowledgeable committee to lead me through this journey. Thank you to everyone I did not mention that assisted me in this exploration. I could not have completed this process without your support, and I am truly grateful. A special thank you to my Academic Advisor, Dr. Lynn Collen for your guidance and direction, and to all my family for always being there for me.