GBAD

                                           Graph-Based Anomaly Detection                                       

• O. A. Ekle and W. Eberle, "Anomaly Detection in Dynamic Graphs: A Comprehensive Survey." ACM Transactions in Knowledge Discovery from Data 18, 8, Article 192, 44 pages. September 2024. https://doi.org/10.1145/3669906

• O. Ekle and W. Eberle, "Dynamic PageRank with Decay: A Modified Approach for Node Anomaly Detection in Evolving Graph Streams," The International FLAIRS Conference Proceedings, Vol. 37, May 2024.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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 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.

• 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.

• 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.

• 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, 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.

• 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.

• 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.

• 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. “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.

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

• 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.