Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Doak, Justin E."'
Publikováno v:
Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131W (18 May 2020)
Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not a
Externí odkaz:
http://arxiv.org/abs/2005.09787
Autor:
Doak, Justin E., Ingram, Joe B., Mulder, Sam A., Naegle, John H., Cox, Jonathan A., Aimone, James B., Dixon, Kevin R., James, Conrad D., Follett, David R.
Publikováno v:
This will be in the proceedings of the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, USA
A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques
Externí odkaz:
http://arxiv.org/abs/1712.07671
Autor:
Smith, Michael R., Ingram, Joe B., Lamb, Christopher C., Draelos, Timothy J., Doak, Justin E., Aimone, James B., James, Conrad D.
It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviat
Externí odkaz:
http://arxiv.org/abs/1711.03947
Autor:
Pham, Tien, Solomon, Latasha, Rainey, Katie, Doak, Justin E., Smith, Michael R., Ingram, Joey B.
Publikováno v:
Proceedings of SPIE; April 2020, Vol. 11413 Issue: 1 p114131W-114131W-17, 11298987p
Publikováno v:
2013 12th International Conference on Machine Learning & Applications; 2013 Volume 2, p34-39, 6p