GitHub content:
Presentations/Publications:
- 2025: EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers. Presented at KDD. August 2025. [Paper, Model, Application, Data]
- 2025: MalwareDB: An Open-Source Bookkeeping System for Malicious and Benign Files. Presented at Shmoocon Firetalks. January 2025. [Slides, Second place winner!]
- 2024: Is Function Similarity Over-Engineered? Building a Benchmark. Presented at The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track, December 2024. [Paper]
- 2024: Enarx & Steward Attestation. Presented at EuroProofNet Tutorial on Usable Formal Methods for Security of Systems, March 2024, Dresden, Germany. [Slides, Video]
- 2022: Minimizing Compute Costs: When Should We Run More Expensive Malware Analysis? Presented at Camlis 2022, October 2022, Arlington, Virginia. [Video, Paper]
- 2022: Cryptle & Hack Challenge. Presented at the Crypto & Privacy Village at Defcon 30. [Slides]
- 2022: No Peakin, No Tweakin'. Presented at the Crypto & Privacy Village at Defcon 30. [Slides]
- 2021: The Haiku Package Manager. Presented at the first PackagingCon. [Slides, Slides & Notes, Video]
- 2021: Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection. In The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI). [arXiv, Code]
- 2020: Creating Cybersecurity Knowledge Graphs From Malware After Action Reports. IEEE Access, Volume 8. [IEEE]
- 2020: Automatic Yara Rule Generation Using Biclustering. 13th ACM Workshop on Artificial Intelligence and Security (AISec’20). [arXiv, Code, Best Paper!]
- 2019: RelExt: relation extraction using deep learning approaches for cybersecurity knowledge graph improvement. ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. [Link]
- 2019: KiloGrams: Very Large N-Grams for Malware Classification. To Appear in KDD Workshop on Learning and Mining for Cybersecurity (LEMINCS). [Pre-print, Code]
- 2018: Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus. In The 13th International Conference on Malicious and Unwanted Software (MALWARE) (pp. 3-12). IEEE. [arXiv, Best Paper!]
- 2017: What can N-Grams Learn for Malware Detection?. In 2017 12th International Conference on Malicious and Unwanted Software (MALWARE) (pp. 109–118). IEEE. [Pre-print]
- 2016: An investigation of byte n-gram features for malware classification. Journal of Computer Virology and Hacking Techniques. doi:10.1007/s11416-016-0283-1. [Official Link, Official-shared, Post-print]
Misc:
