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Intelligent Cyber-Systems and Analytics Research Laboratory

The next-generation battlefield will be populated with a vast number of interconnected, heterogeneous and sometimes autonomous agents including devices, networks, software, and humans. Defending such complex and/or autonomous systems will be impossible for humans to do alone, making our research key in defending such system.

In response to the ongoing and future challenges facing the Cyber and Information Domain, our research directly supports the Army and DoD enterprise in the research, development, experimentation, testing, evaluation and operationalization of computationally intelligent, assured (secure, resilient, robust, safe, trusted), and distributed decision-support systems for autonomous cyber operations in highly-contested, complex battlefield environments. To build, assess and deploy smart, autonomous cyber-systems that enable intelligent, assured and federated decision-making, our research explores the science of information, computation, learning, and fusion for adaptive, collaborative pattern discovery, reasoning, perception, action and decision-making given heterogenous, complex, disparate data spanning devices, networks, software, and humans.

Our research aims to develop models and tools for collective intelligence, likely augmented by interacting with human cyber analysts and decision-makers. In conducting basic and applied research in the areas of data science, operations research, artificial intelligence, cognitive science, computing and advanced analytics, our research seeks to tackle a multitude of challenges in infrastructure and architecture engineering, individual and collective decision-making, stealth and resilience, as well as society. Specifically, our research aims to provide new capabilities to:

  • Shift emphasis from sensing to information awareness
  • Understand the underpinning of autonomy
  • Relieve human cognitive overload in dealing with the data deluge problem
  • Enhance human-machine interface in information processing
  • Cope with various complex disparate data/information types
  • Integrate a diversity of unique reasoning and learning components collaborating simultaneously
  • Bridge correlational with causal discovery
  • Determine solutions or obstructions to local-to-global data fusion problems
  • Mechanize reasoning/learning and computing in the same computational environment
  • Yield provably efficient procedures to enable or facilitate advanced data analytics
  • Deal with high-dimensional and massive datasets with provably guaranteed performance


De Lucia, M., Maxwell, P., Bastian, N., Swami, A., Jalaian, B. & Leslie, N. (2021). Machine Learning for Raw Network Traffic Detection. Proceedings of the 2021 SPIE Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III (Ed. Pham et al., 117460V), pp. 117460V-1 – 117460V-10, SPIE Defense + Commercial Sensing (Volume: 11746).

Painter, C. & Bastian, N. (2021). Generating Genetic Engineering Linked Indicator Datasets for Machine Learning Classifier Training in Biosecurity. Proceedings of the 2021 SPIE Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III (Ed. Pham et al., 1174624), pp. 1174624-1 – 1174624-6, SPIE Defense + Commercial Sensing (Volume: 11746).

Courtoy, J. & Bastian, N. (2021). Three Things Leaders Need to Know Before Investing in Artificial Intelligence. Phalanx, 54(1): 32-37.

Devine, S. & Bastian, N. (2021). An Adversarial Training Based Machine Learning Approach to Malware Classification under Adversarial Conditions. Proceedings of the 54th Hawaii International Conference on System Sciences, pp. 827-836. ScholarSpace.

Kerwin, K. & Bastian, N. (2020). Stacked Generalizations in Imbalanced Fraud Data Sets using Resampling Methods. Journal of Defense Modeling and Simulation. OnlineFirst, 1-18.

Shipp, T., Clouse, D., De Lucia, M., Ahiskali, M., Steverson, K., Mullin, J. & Bastian, N. (2020). Advancing the Research and Development of Assured Artificial Intelligence and Machine Learning Capabilities. Proceedings of the AAAI Fall 2020 Symposium on AI in Government and Public Sector. arXiv:2009.13250.

Bastian, N. (2020). Building the Army’s Artificial Intelligence Workforce. The Cyber Defense Review, 5(2): 55-59.

Chalé, M., Bastian, N. & Weir, J. (2020). Algorithm Selection Framework for Cyber Attack Detection. Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning, pp. 59-63. ACM Digital Library.

Wilkinson, C., Bastian, N. & Kwon, M. (2020). Beyond Traditional Architecture for MDO Applications: the Erlang VM and its Potential. Proceedings of the 2020 SPIE Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II (Ed. Pham et al.), pp. XX-XX. SPIE Defense and Commercial Sensing (Volume: DL 11413).

Bastian, N., Lunday, B., Fisher, C. & Hall, A. (2020). Models and Methods for Workforce Planning Under Uncertainty: Optimizing U.S. Army Cyber Branch Readiness and Manning. Omega, 92(102171): 1-13.

Paul Maxwell, “Artificial Intelligence is the Future of Warfare (Just not in the way you think),” Modern War Institute, 20 Apr 2020.

Mitsopoulos, Somers, Lebiere & Thomson (2020). Cognitive Architectures for Introspecting Deep Reinforcement Learning Agents. In the International Conference on Learning Representations (ICLR) 2020 Workshop on "Bridging AI and Cognitive Science”.

Thomson & Schoenherr (2020). Knowledge-to-Information Translation Training (KITT): An Adaptive Approach to Explainable Artificial Intelligence. In Human Computer Interaction International 2020 Conference.

Paul Maxwell, Elie Alhajjar, and Nathaniel D. Bastian, “Intelligent Feature Engineering for Cybersecurity,” Big Cyber 2019 Workshop in IEEE 2019 Big Data Proceedings, Los Angeles, CA, Dec. 2019, pp. 5005-5011.

Kiely, T. & Bastian, N. (2020). The Spatially Conscious Machine Learning Model. Statistical Analysis and Data Mining: The ASA Data Science Journal, 13(1): 31-49.

Robbins, M., Jenkins, P., Bastian, N. & Lunday, B. (2020). Approximate Dynamic Programming for the Aeromedical Evacuation Dispatching Problem: Value Function Approximation using Multiple Level Aggregation, Omega, 91(102020): 1-17.

Bastian, N., Fisher, C., Hall, A. & Lunday, B. (2019). Solving the Army’s Cyber Workforce Planning Problem using Stochastic Optimization and Discrete-Event Simulation Modeling. Proceedings of the 2019 Winter Simulation Conference (Ed. Mustafee et al.), pp. 738-749. IEEE.

Shetty, S., Ray, I., Celik, N., Mesham, M., Bastian, N. & Zhu, Q. (2019). Simulation for Cyber Risk Management – Where are we, and where do we want to go. Proceedings of the 2019 Winter Simulation Conference (Ed. Mustafee et al.), pp. 726-737. IEEE.

Bastian, N. (2019). Information Warfare and its 18th and 19th Century Roots. The Cyber Defense Review, 4(3): 31-37.

Mukhopadhyay, S., & Fletcher, D. (2018). Generalized Empirical Bayes Modeling via Frequentist Goodness of Fit. Scientific Reports (Nature Publisher Group), 8(1), 9983.

Paradarami, T. K., Bastian, N. D., & Wightman, J. L. (2017). A hybrid recommender system using artificial neural networks. Expert Systems with Applications, 83, 300-313.