RJ Joyce

RJ Joyce

Teaching professor at UMBC. Malware Researcher.

About Me

I recieved my PhD in Computer Science at UMBC in 2024, and I’m currently an assistant teaching professor there. My research is at the intersection of malware analysis, data science, and machine learning. I formerly worked in cybersecurity roles at Booz Allen Hamilton, Cisco Systems, and the Shadowserver Foundation.

First‑Author Research

  • EMBER2024 — A Benchmark Dataset for Holistic Evaluation of Malware Classifiers. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025. (Paper) (GitHub)

  • Claravy — A Tool for Scalable and Accurate Malware Family Labeling. In Proceedings of the ACM on Web Conference, 2025. (Paper) (GitHub)

  • MalDICT: Benchmark Datasets on Malware Behaviors, Platforms, Exploitation, and Packers. In Proceedings of the Conference on Applied Machine Learning in Information Security, 2023. (Paper) (GitHub)

  • AVScan2Vec — Feature Learning on Antivirus Scan Data for Production‑Scale Malware Corpora. In Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023. (Paper) (GitHub)

  • MOTIF — A Malware Reference Dataset with Ground Truth Family Labels. In Computers & Security, vol. 124, 2023. (Paper) (GitHub)

  • Rank‑1 Similarity Matrix Decomposition For Modeling Changes in Antivirus Consensus Through Time. In Proceedings of the Conference on Applied Machine Learning for Information Security, 2021. (Paper)

  • A Framework for Cluster and Classifier Evaluation in the Absence of Reference Labels. In Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, 2021. (Paper)

  • Malware Attribution Using the Rich Header. Presented at ShmooCon 2019. (Paper) (GitHub)

Co‑Authored Research

  • Ransomware Evolution: Unveiling Patterns Using HDBSCAN. In Proceedings of the Conference on Applied Machine Learning in Information Security, 2024. (Paper)

  • Evaluating Representativeness in PDF Malware Datasets: A Comparative Study and a New Dataset. In Proceedings of the IEEE International Conference on Big Data, 2023 (Paper)

  • Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection In ACM Transactions on Privacy and Security, Volume 26, Issue 4, 2023. (Paper)

  • Malware Antivirus Scan Pattern Mining via Tensor Decomposition. (Paper)

PhD Dissertation

  • Investigating Antivirus Scan Results as a Source of Features and Labels for Machine Learning (Dissertation)

Master’s Thesis

  • Evaluating Automatic Malware Classifiers in the Absence of Reference Labels (Thesis)

Patents

  • System and method for converting antivirus scan to a feature vector. (Patent)

  • System and method for modeling correlation in a sourcing model using similarity matrix decomposition. (Patent)

  • Evaluating automatic malware classifiers in the absence of reference labels. (Patent)