I will be listing my publications and other talks here. Hopefully I'll collect some more over time!



  • Raff, E., & Nicholas, C. K. (2017). Lempel-Ziv Jaccard Distance, an Effective Alternative to Ssdeep and Sdhash. arXiv Preprint arXiv:1708.03346. [arXiv] [bibtex]

Peer Reviewed:

  • Raff, E., & Nicholas, C. K. (2017). Malware Classification and Class Imbalance via Stochastic Hashed LZJD. To appear in AISec'17 
  • Raff, E., Sylvester, J., & Nicholas, C. (2017). Learning the PE Header, Malware Detection with Minimal Domain Knowledge. To appear in AISec'17 [arXiv] [bibtex]
  • Zak, R. Raff, E., & Nicholas, C. K. (2017). What can N-Grams Learn for Malware Detection?. To appear in Malicious and Unwanted Software
  • Raff, E., & Nicholas, C. K. (2017). An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. http://doi.org/10.1145/3097983.3098111 [official-pdf] [pre-print] [bibtex]

  • Raff, E. (2017). JSAT: Java Statistical Analysis Tool, a Library for Machine Learning. Journal of Machine Learning Research, 18(23), 1–5. Retrieved from http://jmlr.org/papers/v18/16-131.html [official link] [bibtex] [pdf]
  • Raff, E., Zak, R., Cox, R., Sylvester, J., Yacci, P., Ward, R., … Nicholas, C. (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] [bibtex]

Talks & Posters:

  • Raff, E., Sylvester, J., & McLean, M. (2016). Fighting Malware with Machine Learning. In GPU Technlogy Conference. Washington, D.C.: NVIDIA. [webpage] [pdf]