Research
Dr. Sean T. Miller’s research focuses on the intersection of applied machine learning, cybersecurity, and botnet detection, with a particular emphasis on leveraging advanced computational techniques to enhance security protocols. His work explores innovative methodologies such as Multi-Perspective Machine Learning (MPML), an ensemble learning approach that improves detection accuracy in network intrusion and botnet activity. Through extensive studies, Dr. Miller has examined the impact of different botnet flow feature subsets, the role of explainable AI in cybersecurity, and the potential of Large Language Models (LLMs) in source code analysis for threat detection. His contributions, frequently in collaboration with Dr. Curtis Busby-Earle, have led to significant insights in the field, with highly cited publications in international conferences and journals. Dr. Miller’s research continues to push the boundaries of AI-driven cybersecurity, striving to develop more interpretable, robust, and adaptive security mechanisms.
Research Papers
Dr. Sean T. Miller’s research publications explore the application of machine learning and artificial intelligence in cybersecurity, with a strong focus on botnet detection, intrusion detection systems, and explainable AI. Below is a selection of his key research papers, highlighting his contributions to the field:
2016
The Role of Machine Learning in Botnet Detection
Authors: Sean Miller, Curtis Busby-Earle
Publication date: 2016/12/5
Conference: 2016 11th international conference for internet technology and secured transactions (icitst)
Full Paper Link: Paper
2016
The Impact of different Botnet flow Feature subsets on Prediction Accuracy using Supervised and Unsupervised Learning Methods
Authors: Sean Miller, Curtis Busby-Earle
Publication date: 2016/6
Journal: International Journal of Internet Technology and Secured Transactions
Full Paper Link: Paper
2017
Multi-perspective Machine Learning a Classifier Ensemble Method for Intrusion Detection
Authors: Sean T Miller, Curtis Busby-Earle
Publication date: 2017/1/13
Book: Proceedings of the 2017 international conference on machine learning and soft computing
Full Paper Link: Paper
2017
Multi-perspective Machine Learning (MPML)—A Machine Learning Model for Multi-faceted Learning Problems
Authors: Sean Tavarez Miller, Curtis Busby-Earle
Publication date: 2017/12/14
Conference: 2017 International Conference on Computational Science and Computational Intelligence (CSCI)
Full Paper Link: Paper
2022
Explaining Machine Learning Predictions in Botnet Detection
Authors: Sean Miller, Curtis Busby-Earle
Publication date: 2022/6/19
Book: International Conference on Artificial Intelligence and Soft Computing
Full Paper Link: Link
2024
Towards Establishing the Role of LLMs in Botnet Detection: Effective Prompts for Source Code Analysis
Authors: Sean T Miller, Curtis Busby-Earle
Publication date: 2024/11/6
Book: Proceedings of the Future Technologies Conference
Full Paper Link: Paper
Under Review
Multi-Perspective Machine Learning: Enhancing Interpretability for Heart Disease Predictions
Authors: Sean T Miller, Keaton Logan, Ricardo Anderson, Curtis Busby-Earle, Patricia E Cowell, Lisa-Dionne Morris
Journal: Machine Learning with Applications