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Intelligent Cyber-Systems and Analytics Research Laboratory

Our research directly supports the overarching ACI research initiatives of cyber autonomy, cyber resiliency for critical infrastructures, the future of technology and defense computing, and cyber impacts on physical systems. As part of our research agenda, we conduct basic and applied cyber research in the areas of:

  • Artificial intelligence (logic-based, knowledge-based, probabilistic methods, machine/deep learning, evolutionary computation)
  • Data science (data fusion, data wrangling, data mining, data visualization, big data analytics, network science, computational statistics)
  • Operations research (optimization, probability theory, modeling and simulation, decision science, uncertainty quantification)
  • Cognitive science (knowledge representation, human-machine interfacing, information processing, intelligence/behavior modeling)
  • Computing (high-performance, distributed, cloud/fog/edge, quantum)

In support of the Army and DoD Cyber Enterprise, our research focuses on the design, development, experimentation, testing, evaluation and operationalization of computationally intelligent, assured, large-scale, and distributed decision-support systems for autonomous cyber operations. To build, assess and deploy autonomous cyber-systems that enable intelligent and assured cybersecurity data analytics, our research team explores the science of information, computation, learning, and fusion for adaptive, collaborative pattern discovery, reasoning, perception, action and decision-making given heterogenous, complex, disparate cyber data.

Our research aims to provide the Army and DoD Cyber Enterprise new capabilities to:

  • Shift emphasis from sensing to information awareness
  • Understand the underpinning of cyber autonomy
  • Relieve human cybersecurity analyst cognitive overload in dealing with the data deluge problem
  • Enhance human-machine interface in information processing

Our research has the potential to:

  • 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 cyber data analytics
  • Deal with high-dimensional and massive cyber datasets with provably guaranteed performance

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

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.