Publications

2022

• G. Stone, D. Talbert, and W. Eberle, “A Survey of Scalable Reinforcement Learning,” International Journal of Intelligent Computing Research (IJICR), Volume 13, Issue 1, 2022.
• Islam, S. R., Russell, I., Eberle, W., and Dicheva, D. (2022). “Instilling conscience about bias and fairness in automated decisions,” Journal of Computing Sciences in Colleges, 37(8), 22-31.
• G. Stone, D. Talbert, and W. Eberle, “Utilizing Real-Time Strategy for Penetration Testing,” International Journal of Chaotic Computing (IJCC), Volume 8, Issue 1, 2022.
• P. Lamichhane and W. Eberle, “Self-Organizing Map-Based Graph Clustering and Visualization on Streaming Graphs,” 2022 IEEE International Conference on Data Mining Workshops (ICDMW), November 2022
• S.R. Islam and W. Eberle, “Domain Knowledge-Aided Explainable Artificial Intelligence”. In: Ahmed, M., Islam, S.R., Anwar, A., Moustafa, N., Pathan, AS.K. (eds) Explainable Artificial Intelligence for Cyber Security. Studies in Computational, 2022 Intelligence, vol 1025. Springer, Cham. https://doi.org/10.1007/978-3-030-96630-0_4
• P. Lamichhane, H. Mannering, and W. Eberle, “Discovering Breach Patterns on the Internet of Health Things: A Graph and Machine Learning Anomaly Analysis”, International Conference of the Florida Artificial Intelligence Research Society (FLAIRS), May 2022.
• S. R. Islam, I. Russell, W. Eberle, and D. Dicheva, “Incorporating the Concepts of Fairness and Bias into an Undergraduate Computer Science Course to Promote Fair Automated Decision Systems,” SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education, March 2022.

2021

• P. Lamichhane and W. Eberle, “Anomaly Detection in Edge Streams Using Term Frequency-Inverse Graph Frequency (TF-IGF) Concept,” IEEE Big Data, December 2021.
• R. Paudel, L. Tharp, D. Kaiser, W. Eberle, and G. Gannod, “Visualization of Anomalies using Graph-Based Anomaly Detection,” International Conference of the Florida Artificial Intelligence Research Society (FLAIRS), May 2021.
• S. R. Islam and W. Eberle, “Implications of Combining Domain Knowledge in Explainable Artificial Intelligence,” Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE), March 2021.
• G. Stone, D. Talbert, and W. Eberle, “Using AI/Machine Learning for Reconnaissance Activities During Network Penetration Testing,” 16th International Conference of Cyber Warfare and Security (ICCWS), Feb 24-26, 2021.

2020

• W. Eberle and L. Holder, “Graph Filtering to Remove the “Middle Ground” for Anomaly Detection”, IEEE Big Data Conference, Workshop on High Performance Big Graph Data Management, Analysis, and Mining (BigGraphs 2020), December 2020.
• R. Paudel and W. Eberle. “An Approach For Concept Drift Detection in a Graph Stream Using Discriminative Subgraphs.” ACM Transactions on Knowledge Discovery from Data. Article 70. September 2020. DOI:https://doi.org/10.1145/3406243.
• R. Paudel and W. Eberle, “SNAPSKETCH: Graph Representation Approach for Intrusion Detection in a Streaming Graph,” Conference on Knowledge Discovery and Data Mining (KDD) Mining and Learning with Graphs (MLG), August 2020.
• S. R. Islam, W. Eberle, and S. Ghafoor, “Towards Quantification of Explainability in Explainable Artificial Intelligence Methods,” International Conference of the Florida Artificial Intelligence Research Society (FLAIRS), May 2020.
• P. Kandel and W. Eberle, “Node Similarity For Anomaly Detection in Attributed Graphs,” International Conference of the Florida Artificial Intelligence Research Society (FLAIRS), May 2020.
• S. R. Islam, W. Eberle, S. Ghafoor, A. Siraj, and M. Rogers, “Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response,” Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE), March 2020.

2019

• Ramesh Paudel, Prajwal Kandel, and William Eberle, “Detecting Spam Tweets in Trending Topics using Graph-Based Approach,” Proceedings of the Future Technologies Conference (FTC), October 2019.
• Sheikh Rabiul Islam, William Eberle, Sid C. Bundy, and Sheikh Ghafoor, “Infusing Domain Knowledge in AI-based “black box” Models for Better Explainability with Application in Bankruptcy Prediction,” Workshop on Anomaly Detection in Finance, SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), August 2019.
• A. Bhuiyan, M. B. Sharif, P. J. Tinker, W. Eberle, D. A. Talbert, S. K. Ghafoor, and L. Frey, “Gene Selection and Clustering of Breast Cancer Data,” International Conference of the Florida AI Research Society (FLAIRS), May 2019.
• Sirisha Velampalli, Lenin Mookiah, and William Eberle, “Discovering Suspicious Patterns Using a Graph Based Approach,” International Conference of the Florida AI Research Society (FLAIRS), May 2019.
• Ramesh Paudel, Peter Harlan, and William Eberle, “Detecting the Onset of a Network Layer DoS Attack with a Graph-Based Approach,” International Conference of the Florida AI Research Society (FLAIRS), May 2019.

2018

• Lenin Mookiah, William Eberle, and M. Mondal, “Personalized News Recommendation using Graph-Based Approach”, Intelligent Data Analysis, an International Journal, Volume 22 (2018) 881–909.
• Ramesh Paudel, William Eberle, and Lawrence Holder, “Anomaly Detection of Elderly Patient Activities in Smart Homes using a Graph-Based Approach,” International Conference on Data Science (ICDATA), July 2018.
• Sheikh Rabiul Islam, William Eberle, and Sheikh Ghafoor, “Credit Default Mining Using Combined Machine Learning and Heuristic Approach,” International Conference on Data Science (ICDATA), July 2018.
• Rina Singh, Jeffrey Graves, Douglas Talbert, and William Eberle, “Prefix and Suffix Sequential Pattern Mining”, Conference on Machine Learning and Data Mining (MLDM), July, 2018.
• Ramesh Paudel, Kimberlyn Dunn, William Eberle, and Danielle Chaung, “Cognitive Health Prediction on the Elderly Using Sensor Data in Smart Homes,” International Conference of the Florida AI Research Society (FLAIRS), May 2018.

2017

• Sirisha Velampalli, Lenin Mookiah, and William Eberle, “Detecting Vehicular Patterns Using a Graph-Based Approach,” IEEE Conference on Visual Analytics Science and Technology (VAST), October 2017.
• Deon Liang, Chih-Fong Tsai, An-Jie Dai, and William Eberle, “A novel classifier ensemble approach for financial distress prediction,” Knowledge and Information Systems, An International Journal, May 2017.
• Lenin Mookiah, Chris Dean, and William Eberle, “Graph-Based Anomaly Detection on Smart Grid Data,” International Conference of the Florida AI Research Society (FLAIRS), May 2017.
• Ramesh Paudel, William Eberle, and Douglas Talbert “Detection of Anomalous Activity in Diabetic Patients Using Graph-Based Approach,” International Conference of the Florida AI Research Society (FLAIRS), May 2017.
• Sirisha Velampalli and William Eberle, “Novel Graph Based Anomaly Detection Using Background Knowledge,” International Conference of the Florida AI Research Society (FLAIRS), May 2017.
• Nishith Thakkar, Lenin Mookiah, Douglas Talbert, and William Eberle, “Anomalies in Student Enrollment Using Visualization,” International Conference of the Florida AI Research Society (FLAIRS), May 2017.

2016

• Zdravko Markov, Ingrid Russell, and William Eberle, “Report on the 29th International Florida Artificial Intelligence Research Society Conference (FLAIRS-29),” AI Magazine, Winter 2016.
• Lenin Mookiah and William Eberle, “Co-Ranking Authors in Heterogeneous News Networks,” 2016 International Conference on Computational Science and Computational Intelligence, December 2016.
• Sirisha Velampalli and William Eberle, “Novel Application of MapReduce and Conceptual Graphs,” 2016 International Conference on Computational Science and Computational Intelligence, December 2016.
• William Eberle and Lawrence Holder, “Identifying Anomalies in Graph Streams Using Change Detection,” Conference on Knowledge Discovery and Data Mining (KDD) Mining and Learning with Graphs (MLG), August 2016.
• Lenin Mookiah, William Eberle, and Maitrayi Mondal, “Detecting Change in News Feeds Using a Context-Based Graph,” International Conference on Data Mining (DMIN), 2016.
• Dario Cruz, Doug Talbert, William Eberle, and Joseph Biernacki, “A neural network approach for predicting microstructure development in cement,” Int’l Conf. Artificial Intelligence, ICAI’16, pp. 328-334, 2016.
• M.-W. Huang, W.-C. Lin, L. Chih-Wen, C. Shih-en, C.-F. Tsai, and W. Eberle, “Data preprocessing issues for incomplete medical datasets,” Expert Systems, June 2016.

2015

• Lenin Mookiah, William Eberle, and Lawrence Holder, “Discovering Suspicious Behavior Using Graph-Based Approach,” International Conference of the Florida AI Research Society (FLAIRS), May 2015. (Nominated for Best Student Paper)
• Cameron Chaparro and William Eberle, “Detecting Anomalies in Mobile Telecommunication Networks Using a Graph Based Approach,” International Conference of the Florida AI Research Society (FLAIRS), May 2015.
• Lenin Mookiah, William Eberle, and Ambareen Siraj, “Survey of Crime Analysis and Prediction,” International Conference of the Florida AI Research Society (FLAIRS), May 2015.
• William Eberle and Lawrence Holder, “Streaming Data Analytics for Anomalies in Graphs,” IEEE International Symposium on Technologies for Homeland Security, April 2015.
• William Eberle and Lawrence Holder. “Scalable Anomaly Detection in Graphs,” Intelligent Data Analysis, an International Journal, Volume 19(1), 2015.
• W.-C. Lin, C.-F. Tsai, S.-W. Ke, C.-W. Hung, and W. Eberle, “Learning to Detect Representative Data for Large Scale Instance Selection,” Journal of Systems and Software, 2015.

2014

• Vitaly Ford, Ambareen Siraj, and William Eberle. “Smart Energy Fraud Detection Using Artificial Neural Networks,” IEEE Symposium Series on Computational Intelligence (SSCI), December 2014.
• Lenin Mookiah, William Eberle, and Lawrence Holder. “Detecting Suspicious Behavior Using a Graph-Based Approach,” IEEE Symposium on Visual Analytics Science and Technology (VAST), November 2014.
• William Eberle and Lawrence Holder. “A Partitioning Approach to Scaling Anomaly Detection in Graph Streams,” First International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (BigGraphs), IEEE BigData Conference, October 2014.
• Z.-Y. Chen, C.-F. Tsai, W. Eberle, W.-C. Lin, and S.-W. Ke. “Instance Selection by Genetic-Based Biological Algorithm,” Soft Computing, June 2014.

2013

• William Eberle and Lawrence Holder. “Incremental Anomaly Detection in Graphs,” Proceedings of the IEEE ICDM Workshop on Incremental Clustering, Concept Drift and Novelty Detection (IcIaNov), December 2013.
• William Eberle, Douglas Talbert, Eric Simpson, Larry Roberts, and Alexis Pope. “Using Machine Learning and Predictive Modeling to Assess Admission Policies and Standards,” 9th Annual National Symposium on Student Retention, November 2013.
• William Eberle, John Karro, Neal Lerner, and Matthias Stallmann. “Integrating Communication Skills in Data Structures and Algorithms Courses,” Frontiers in Education (FIE) Conference, October 2013.
• Chris Morack and William Eberle. “Computer Science Widening the STEM Education Spectrum,” Frontiers in Education (FIE) Conference, October 2013.
• Alan McCormick and William Eberle, “Discovering Fraud in Online Classified Ads,” International Conference of the Florida AI Research Society (FLAIRS), May 2013.

2012

• Chih-Fong Tsai, William Eberle and Chi-Yuan Chu. “Genetic Algorithms in Feature and Instance Selection,” Knowledge-Based Systems, Volume 39, pp. 240-247, 2012.
• William Eberle, Lawrence Holder and Beverly Massengill. “Graph-Based Anomaly Detection Applied to Homeland Security Cargo Screening,” International Conference of the Florida AI Research Society (FLAIRS), May 2012.

2011

• Brandon Sherrill, William Eberle and Douglas Talbert. “Analysis of Student Data for Retention Using Data Mining Techniques,” National Symposium of Student Retention (NSSR), November 2011.
• William Eberle and Lawrence Holder. “Compression versus Frequency for Mining Patterns and Anomalies in Graphs,” Conference on Knowledge Discovery and Data Mining (KDD) Mining and Learning with Graphs (MLG), August 2011.
• William Eberle and Lawrence Holder. “Graph-Based Knowledge Discovery: Compression versus Frequency,” International Conference of the Florida AI Research Society (FLAIRS), May 2011.
• William Eberle, Lawrence Holder and Jeffrey Graves. “Insider Threat Detection Using a Graph-based Approach,” Journal of Applied Security Research, Volume 6, Issue 1, January 2011.

2010

• William Eberle, Lawrence Holder and Jeffrey Graves. “Using a Graph-Based Approach for Discovering Cybercrime,” International Conference of the Florida AI Research Society (FLAIRS), May 2010.

2009

• William Eberle, Lawrence Holder and Jeffrey Graves. “Detecting Employee Leaks Using Badge and Network IP Traffic,” IEEE Symposium on Visual Analytics Science and Technology (VAST), October 2009.
• William Eberle and Lawrence Holder. “Applying Graph-based Anomaly Detection Approaches to the Discovery of Insider Threats,” IEEE International Conference on Intelligence and Security Informatics (ISI), June 2009.
• William Eberle and Lawrence Holder. “Discovering Anomalies to Multiple Normative Patterns in Structural and Numeric Data,” International Conference of the Florida AI Research Society (FLAIRS), May 2009. Best Paper Award.
• William Eberle, Lawrence Holder and Diane Cook. “Identifying Threats Using Graph-Based Anomaly Detection” In Machine Learning in Cyber-Trust, J. Tsai and P. Yu (eds.), May 2009.
• William Eberle and Lawrence Holder. “Graph-Based Approaches to Insider Threat Detection,” Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research (CSIIRW), April 13-15, 2009.
• William Eberle and Lawrence Holder. “Mining for Insider Threats in Business Transactions and Processes,” Computational Intelligence in Data Mining (CIDM), IEEE Symposium Series on Computational Intelligence, March 30-April 2, 2009.
• William Eberle and Lawrence Holder. “Insider Threat Detection Using Graph-Based Approaches,” Cybersecurity Applications and Technologies Conference for Homeland Security (CATCH), March 2009.

2008

• William Eberle and Lawrence Holder. “Analyzing Catalano/Vidro Social Structure Using GBAD,” VAST 2008 Challenge Track, VisWeek, October 2008.

2007

• William Eberle and Lawrence Holder. “Anomaly Detection in Data Represented as Graphs,” Intelligent Data Analysis: An International Journal, Volume 11, Number 6, pp. 663-689. 2007.
• William Eberle and Lawrence Holder. “Discovering Structural Anomalies in Graph-Based Data,”  Mining Graphs and Complex Structures Workshop, IEEE International Conference on Data Mining (ICDM), October 2007.
• William Eberle and Lawrence Holder.  “Mining for Structural Anomalies in Graph-Based Data,” International Conference on Data Mining (DMIN), June 2007.
• William Eberle. “Information Theoretic, Probabilistic, and Maximum Partial Substructure Algorithms for Discovering Graph-Based Anomalies,” Doctoral Dissertation. University of Texas at Arlington. May 2007.

2006

• William Eberle and Lawrence Holder. “Detecting Anomalies in Cargo Using Graph Properties,”  IEEE International Conference on Intelligence and Security Informatics, May, 2006.