Machine Learning Applied to Security

Tello, B., Winterrose, M., Baah, G., Zhivich, M., Simulation Based Evaluation of a Code Diversification Strategy, Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, Colmar, Alsace, France, July, 2015.
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Noel, S., Ludwig, J., Jain, P., Johnson, D., Thomas, R., McFarland, J., King, B., Webster, S., Tello, B., Analyzing Mission Impacts of Cyber Actions (AMICA), Proceedings of the NATO S&T Workshop on Cyber Attack Detection, Forensics and Attribution for Assessment of Mission Impact, Istanbul, Turkey, June 2015.
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Wagner N., Lippmann R., Winterrose M., Riordan J., Yu T., and Streilein W., Agent-based Simulation for Assessing Network Security Risk due to Unauthorized Hardware, Proceedings of the 2015 ACM Spring Simulation Multi-Conference - Agent Directed Simulation Symposium, Alexandria, VA, April, 2015.
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Carter K., Riordan J., and Okhravi H., A Game Theoretic Approach to Strategy Determination for Dynamic Platform Defenses, ACM CCS Moving Target Defense Workshop, November 2014.
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Winterrose M., Carter K., Wagner N., Streilein W., Adaptive Attacker Strategy Development Against Moving Target Cyber Defenses, Proceedings of the ModSim World Conference, Hampton, VA, May 2014.
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Spain, M., Fuller, B., Ingols, K., Cunningham, R., Robust Keys from Physical Unclonable Functions, IEEE Symposium on Hardware-Oriented Security and Trust (HOST), Arlington, VA, p. 88-92, May 2014.
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Winterrose M., Carter K., Strategic Evolution of Adversaries Against Temporal Platform Diversity Active Cyber Defenses, Proceedings of the Symposium on Agent Directed Simulation of the Society for Computer Simulation International, April 2014.
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Carter, K. M., Idika, N., Streilein, W. W., Probabilistic Threat Propagation for Malicious Activity Detection, Proceedings of ICASSP 2013, Vancouver, B.C., May 2013.
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Gold, K., Priest, B., Carter, K., An Expectation Maximization Approach to Detecting Compromised Remote Access Accounts, Proceedings of FLAIRS 2013, St. Pete Beach, FL, May 2013.
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Carter, K., Streilein, W., Probabilistic Reasoning for Streaming Anomaly Detection, in Proceedings of IEEE Statistical Signal Processing Workshop, Ann Arbor, MI, August 5, 2012.
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Carter, K., Gold, K., Priest, B., Analytics for Cyber Situational Awareness, 80th Annual MORS Symposium, Colorado Springs, CO, June 14, 2012.
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Laskov, P., Lippmann, R., Machine Learning in Adversarial Environments, Machine Learning Journal, 81, 2010.
(Journal Article)

Wright, C. V., Connelly, C., Braje, T., Rabek, J. C., Rossey, L. M., Cunningham, R. K., Generating Client Workloads and High-Fidelity Network Traffic for Controllable, Repeatable Experiments in Computer Security, RAID 2010, Ottawa, CA, 15-17 September 2010.
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Carter, K. M., Lippman, R. P., Boyer, S. W., Temporally Oblivious Anomaly Detection on Large Networks Using Functional Peers, Internet Measurement Conference 2010, Melbourne, Australia, 3 November 2010, pp. 465-471.
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Lippmann, R.P., Fried, D., Piwowarski, K., Streilein, W., Passive Operating System Identification from TCP/IP Packet Headers. in Proceedings Workshop on Data Mining for Computer Security (DMSEC), 2003.
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Dain, O. M., Cunningham, R. K., Building Scenarios from a Heterogeneous Alert Stream, IEEE Transactions on Systems, Man and Cybernetics, 2002.
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Dain, O.M., Cunningham, R. K., Fusing a Heterogeneous Alert Stream into Scenarios, ACM Computer and Communications Security 2001. Philadelphia, Pennsylvania, USA, Nov. 5–8, 2001.
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Cunningham, R. K., Stevenson, C., Accurately Detecting Source Code of Attacks That Increase Privilege, RAID 2001 Proceedings, Davis, California, USA, pp. 104–116, October, 2001.
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Cunningham, R. K., Reiser, A., Detecting Source Code of Attacks that Increase Privilege, RAID 2000 Proceedings, Toulouse, France, October 2–4, 2000.
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Lippmann, R. P., Cunningham, R. K., Using Key-String Selection and Neural Networks to Reduce False Alarms and Detect New Attacks with Sniffer-Based Intrusion Detection Systems. Second International Workshop on Recent Advances in Intrusion Detection (RAID 1999), West Lafayette, Indiana, 1999.
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Lippmann, R. P., Wyschogrod, D., Webster, S. E., Weber, D. J., Gorton, S., Using Bottleneck Verification to Find Novel New Attacks with a Low False-Alarm Rate, First International Workshop on Recent Advances in Intrusion Detection, Louvain-la-Neuve, Belgium, 1998.
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Lippmann, R.P., Kukolich, L., Shahian D., Predicting the Risk of Complications in Coronary Artery Bypass Operations Using Neural Networks, in Advances in Neural Information Processing Systems 7, G. Tesauro, D. Touretzky, and T. Leen, eds., Morgan Kaufmann: San Mateo, CA, pp. 1055–1062. 1995.
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Lippmann, R.P., Neural Networks, Bayesian a posteriori Probabilities and Pattern Classification, in From Statistics to Neural Networks. Theory and PatternRecognition Applications, V. Cherkassky, J.H. Friedman, and H. Wechsler, eds., Springer-Verlag. 1994.
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Lippmann, R.P., Chang, E.I., Jankowski, C.R., Wordspotter Training Using Figure-Of-Merit Back-Propagation, in International Conference on Acoustics Speech and Signal Processing, Adelaide, Australia. 1994.
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Lippmann, R.P., An Introduction to Computing with Neural Nets, IEEE Acoustical Speech and Signal Processing Magazine, 4, 4–22, 1987. Reprinted in Neural Networks: Theoretical Foundations and Analysis, Edited by Clifford Lau, IEEE Press, 1992. Also reprinted in Optical Neural Networks, Edited by S. Jutamulia, SPIE Optical Engineering Press, 1994.
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Lippmann, R.P., Kukolich, L., and Singer, E. LNKnet: Neural Network, Machine-Learning, and Statistical Software for Pattern Classification, Lincoln Laboratory Journal, 6(2) pp. 249–268. 1993.
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Richard, M. D. and Lippmann, R. P., Neural Network Classifiers Estimate Bayesian a posteriori Probabilities, Neural Computation, 3, pp. 461–483. 1991.
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Kelly, W. J., Lippmann, R. P., Group-Vote Rules for Adaptive Psychological Testing, Journal of the Acoustical Society of America, 906–908, 1979.

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