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RESEARCH LAB OVERVIEW

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 challenges facing the Cyber and Information Domain, our research directly supports the Army, Department of Defense, Intelligence Community, and Nation in the research, design, development, experimentation, testing, evaluation and operationalization of computationally intelligent, assured (secure, resilient, robust, trusted), and distributed decision-support models, tools, and 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, scientific 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

ICSARL Lab

PUBLICATIONS

2023

Monkam, G., De Lucia, M. & Bastian, N. (2023). A Topological Data Analysis Approach for Detecting Data Poisoning Attacks Against Machine Learning Based Network Intrusion Detection Systems. SSRN.

Farrukh, Y., Wali, S., Khan, I. & Bastian, N. (2023). AIS-NIDS: An Intelligent and Self-sustaining Network Intrusion Detection System. SSRN.

Hore, A., Ghadermazi, J., Shah, A., Bastian, N. (2023). A Sequential Deep Learning Framework for a Robust and Resilient Network Intrusion Detection System. SSRN.

Hore, S., Nguyen, Q., Xu, Y., Shah, A., Bastian, N. & Le, T. (2023). Empirical Evaluation of Autoencoder Models for Anomaly Detection in Packet-based NIDS. TechRxiv

Farrukh, Y., Wali, S., Khan, I. & Bastian, N. (2023). Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection. arXiv:2309.07461.

Jha, S., Jha, S., Lincoln, P., Bastian, N., Velasquez, A., Ewetz, R. & Neema, S. (2023). Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis Using Large Language Models and Satisfiability Solving. arXiv:2309.16436.

Berenbeim, A., Bierbrauer, D., Cruickshank, I., Thomson, R., & Bastian, N. (2023). Applications of Certainty Scoring for Machine Learning Classification in Multi-modal Contexts. TechRxiv.

Jha, S., Jha, S., Lincoln, P., Bastian, N., Velasquez, A. & Neema, S. (2023). Dehallucinating Large Language Models Using Formal Methods Guided Iterative Prompting. Proceedings of the 2023 IEEE International Conference on Assured Autonomy, pp. 149-152. IEEE.

Chen, J., Zhang, L., Riem, J., Adam, G., Bastian, N., & Lan, T. (2023). Explainable Learning-Based Intrusion Detection Supported by Memristors. Proceedings of the 2023 IEEE Conference on Artificial Intelligence, pp. 195-196. IEEE.

Baye, G., Silva, P., Broggi, A., Fiondella, L., Bastian, N. & Kul, G. (2023). Performance Analysis of Deep-Learning Based Open Set Recognition Algorithms for Network Intrusion Detection Systems. Proceedings of the 2023 IEEE/IFIP Network Operations and Management Symposium, 1-6. IEEE.

Bierbrauer, D., Schabinger, R., Carlin, C., Mullin, J., Pavlik, J. & Bastian, N. (2023). Autonomous Cyber Warfare Agents: Dynamic Reinforcement Learning for Defensive Cyber Operations. Proceedings of the 2023 SPIE Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V (125380E), pp. 125380E-1 – 125380E-16, SPIE Defense + Commercial Sensing (Volume: 12538).

Premkumar, A., Schneider, M., Spivey, C., Pavlik, J. & Bastian, N. (2023). Graph Representation Learning for Context-Aware Network Intrusion Detection. Proceedings of the 2023 SPIE Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V (125380H), pp. 125380H-1 – 125380H-12, SPIE Defense + Commercial Sensing (Volume: 12538).

Willeke, M., Bierbrauer, D. & Bastian, N. (2023). Data-Efficient, Federated Learning for Raw Network Traffic Detection. Proceedings of the 2023 SPIE Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V (125380Y), pp. 125380Y-1 – 125380Y-17, SPIE Defense + Commercial Sensing (Volume: 12538).

Pavlik, J. & Bastian, N. (2023). Cyber Creative Generative Adversarial Network for Novel Malicious Packets. Proceedings of the 2023 SPIE Conference on Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications (Ed. Howell et al., 125290L), pp. 125290-1 - 125290-11, SPIE Defense + Commercial Sensing (Volume: 12529).

Ghadermazi, J., Shah, A. & Bastian, N. (2023). Towards Real-time Network Intrusion Detection with Image-based Sequential Packets Representation. TechRxiv.

Chalé, M., Cox, B., Weir, J. & Bastian, N. (2023). Constrained Optimization Based Adversarial Example Generation for Transfer Attacks in Network Intrusion Detection Systems. Optimization Letters, Online First: 1-20

Bradley, T., Alhajjar, E. & Bastian, N. (2023). Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification. Proceedings of the 2023 IEEE International Conference on Assured Autonomy, pp. 11-18. IEEE.

Farrukh, Y., Wali, S., Khan, S. & Bastian, N. (2023). SeNet-I: An Approach for Detecting Network Intrusions Through Serialized Network Traffic Images. Engineering Applications of Artificial Intelligence, 126(107169): 1-16.

Maffey, K., Dotterrer, K., Niemann, J., Cruickshank, I., Lewis, G., & Kastner, C. (2023). MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities. Proceedings of the 2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results.

Berenbeim, A., Cruickshank, I., Jha, S., Thomson, R. & Bastian, N. (2023). Measuring Classification Decision Certainty and Doubt. arXiv: 2303.14568.

Hore, S., Ghadermazi, J., Paudel, D., Shah, A., Das, T. & Bastian, N. (2023). Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation. arXiv: 2305.11039.

Bastian, N. (2023). Gaining Competitive Advantages in Cyberspace through the Integration of Breakthrough Technologies in Autonomy, Artificial Intelligence, and Machine Learning. The Cyber Defense Review, 8(1): 55-65.

Bastian, N., Jha, S., Tabuada, P., Veeravalli, V. & Verma, G. (2023). Principles of Robust Learning and Inference for Internet of Battlefield Things. In Robert Douglass, Keith Gremban, Ananthram Swami and Stephan Gerali (Ed.), Internet of Things for Defense and National Security (pp. 119-128). Wiley-IEEE Press.

Bastian, N. & Dinmore, M. (2023). Military and Security Applications: Cybersecurity. In Panos Pardalos and Oleg Prokopyev (Ed.), Encyclopedia of Optimization, Third Edition (pp. 1-10). Springer, Cham.

Bierbrauer, D., De Lucia, M., Reddy, K., Maxwell, P. & Bastian, N. (2023). Transfer Learning for Raw Network Traffic Detection. Expert Systems with Applications, 211(118641): 1-10.

Hore, S., Shah, A. & Bastian, N. (2023). Deep VULMAN: A Deep Reinforcement Learning-enabled Cyber Vulnerability Management Framework. Expert Systems with Applications, 221(119734): 1-17.

2022

Bundt, J., Davinroy, M., Agadakos, I., Oprea, A. & Robertson, W. (2022). Attacking Neural Binary Function Detection. arXiv: 2208.11667.

Lin, Z., Pike, T., Bailey, M. & Bastian, N. (2022). A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System. arXiv:2211.03933.

Abdelzaher, T., Bastian, N., Jha, S., Kaplan, L., Srivastava, M. & Verravalli, V. (2022). Context-Aware Collaborative Neuro-Symbolic Inference in Internet of Battlefield Things. Proceedings of the 2022 IEEE Military Communications Conference, pp. 1053-1058. IEEE.

Alhajjar, E. (2022). Network Science. In Daniel Bennett, Paul Goethals & Natalie Scala (Ed.), Mathematics in Cyber Research (pp. 207-232). Boca Raton, FL: CRC Press.

Byington, N., Davis, C., Meehan, M., Vincent, C., Woodward, D. & Bastian, N. (2022). Counter-AI Tool System Design for AI System Adversarial Testing and Evaluation. Proceedings of the 2022 General Donald R. Keith Memorial Capstone Conference, pp. 52-57. SISE.

Chalé, M. & Bastian, N. (2022). Generating Realistic Cyber Data for Training and Evaluating Machine Learning Classifiers for Network Intrusion Detection Systems. Expert Systems with Applications, 207(117936): 1-18.

Chandak, Y., Shankar, S., Bastian, N., Castro da Silva, B., Brunskill, E. & Thomas, P. (2022). Off-Policy Evaluation for Action-Dependent Non-Stationary Environments. Proceedings of the 36th Conference on Neural Information Processing Systems, pp. 9217-9232.

Goethals, P., Scala, N. & Bastian, N. (2022). Operations Research. In Daniel Bennett, Paul Goethals and Natalie Scala (Ed.), Mathematics in Cyber Research (pp. 233-266). Boca Raton, FL: CRC Press.

Hore, S., Shah, A. & Bastian, N. (2022). An Artificial Intelligence-Enabled Framework for Optimizing the Dynamic Cyber Vulnerability Management Process. Proceedings of the 39th International Conference on Machine Learning, pp. 1-21.

Farrukh, Y., Khan, I., Wali, S., Bierbrauer, D., Pavlik, J. & Bastian, N. (2022). Payload-Byte: A Tool for Extracting and Labeling Packet Capture Files of Modern Network Intrusion Detection Datasets. Proceedings of the 9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, pp. 58-67.

Leehan, B. & Bastian, N. (2022). Geospatial Big Data Analytics for Quality Control of Surveys. Proceedings of the 2022 General Donald R. Keith Memorial Capstone Conference, pp. 262-268. SISE.

Ng, L.H.X., Cruickshank, I.J. & Carley, K.M. (2022). Coordinating Narratives Framework for cross-platform analysis in the 2021 US Capitol riots. Computational and Mathematical Organization Theory.

Pavlik, J.A., Ludden, I.G. and Jacobson, S.H., (2022). SARS-CoV-2 aerosol risk models for the Airplane Seating Assignment Problem. Journal of Air Transport Management, 99, p.102175.

Roy, A., Cobb, A., Bastian, N., Jalaian, B. & Jha, S. (2022). Runtime Monitoring of Deep Neural Networks Using Top-Down Context Models Inspired by Predictive Processing and Dual Process Theory. Proceedings of the AAAI Spring 2022 Symposium on Designing Artificial Intelligence for Open Worlds, pp. 1-11. AAAI.

Smith, J. & Bastian, N. (2022). A Ranked Solution for Social Media Fact Checking Using Epidemic Spread Modeling. Information Sciences, pp. 1-14.

Thomas, D.M., Kleinberg, S., Brown, A.W. et al. (2022) Machine learning modeling practices to support the principles of AI and ethics in nutrition research. Nutrition & Diabetes 12, 48.

2021

Cobb, A., Jalaian, B., Bastian, N. & Russell, S. (2021). Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks. Proceedings of the 2021 Winter Simulation Conference (Ed. Kim et al.), pp. 1-12. IEEE.

Chale, M. & Bastian, N. (2021). Challenges and Opportunities for Generative Methods in the Cyber Domain. Proceedings of the 2021 Winter Simulation Conference (Ed. Kim et al.), pp. 1-12. IEEE.

Schneider, M., Aspinall, D. & Bastian, N. (2021). Evaluating Model Robustness to Adversarial Samples in Network Intrusion Detection. Proceedings of the 2021 IEEE International Conference on Big Data, pp. 3343-3352. IEEE.

Talty, K., Stockdale, J. & Bastian, N. (2021). A Sensitivity Analysis of Poisoning and Evasion Attacks in Network Intrusion Detection System Machine Learning Models. Proceedings of the 2021 IEEE Military Communications Conference, pp. 1017-1022.

Pavlik, J.A., Sewell, E.C. and Jacobson, S.H., (2021). Iterative Deepening Dynamically Improved Bounds Bidirectional Search. INFORMS Journal on Computing.

Pyke, A., Rovira, E., Murray, S., Pritts, J., Carp, C., & Thomson, R. (2021). Responses to Cyber Threats: Arousal, Emotion and Situational Trust Among those who Correctly Identify Them and Those who don't. Journal of Cyber Psychology.

Stefik, M., Youngblood, M., Pirolli, P., Lebiere, C., Thomson, R., Price, R., Nelson, L., Krivacic, R., Le, J., Mitsopoulos, K., Somers, S., & Schooler, J. (2021). Explaining Autonomous Drones: An XAI Journey. Applied AI Letters.

Thomson, R., Somers, P., Mitsopoulos, C., Schooler, J., Lebiere, C., & Pirolli, P. (2021). Towards a Psychology Of Deep Reinforcement Learning Agents Using a Cognitive Architecture. TopiCS in Cognitive Science, Special Issue on Cognitive Approaches to Artificial Intelligence. Cassenti, D & Ritter, F. (Eds).

Schoenherr, J. R., & Thomson, R. (2021). Persuasive Features of Scientific Communication. Explanatory Schemata of Physical and Psychosocial Phenomena. Frontiers in Psychology.

Thomson, R., Cranford, E., & Lebiere, C. (2021). Achieving Active Cybersecurity through Agent-Based Cognitive Models for Detection and Defense. Proceedings of the 1st Annual Autonomous Intelligent Cyber-defense Agent Annual Conference.

Mazzeo, M., Chewning-Kulick, M., Pike, W., Cartwright, J., Rovira, E., & Thomson, R. (2021) Detecting Hesitation During Battlefield Wound treatment on Female Soldiers. Proceedings of the Applied Human Factors and Ergonomics Society Annual Conference.

Schoenherr, J., & Thomson, R. (2021). The Cybersecurity (CSEC) Questionnaire: Individual Differences in Unintentional Insider Threat Behaviors. Cyber Science 2021.

Schoenherr, J., & Thomson, R. (2021) Health Information Seeking Behavior, Risk Communication, and Mobility During COVID-19. IEEE ISTAS 2020.

Alhajjar, E. & Bradley, T. (2021). Survival analysis for insider threat, Computational and Mathematical Organization Theory, 1-17.

Alhajjar, E. et al. (2021). Cubature Kalman Filter Based Training of Hybrid Differential Equation Recurrent Neural Network Physiological Dynamic Models. Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE. To appear.

Alhajjar, E. & Russell, T. (2021). Maximally entangled correlation sets. Houston J. Math. 46 no. 2, 357–376.

Zhang, L., Butrovich, M., Li, T., Pavlo, A., Nannapaneni, Y., Rollinson, J., Zhang, H., Balakumar, A., Biales, D., Dong, Z., Eppinger, E.J., Gonzalez, J.E., Lim, W.S., Liu, J., Ma, L., Menon, P., Mukherjee, S., Nayak, T., Ngom, A., Niu, D., Patra, D., Raj, P., Wang, S., Wang, W., Yu, Y., & Zhang, W. (2021). Everything is a Transaction: Unifying Logical Concurrency Control and Physical Data Structure Maintenance in Database Management Systems. CIDR, January 2021.

Bierbrauer, D., Chang, A., Kritzer, W. & Bastian, N. (2021). Cybersecurity Anomaly Detection in Adversarial Environments. Proceedings of the AAAI Fall 2021 Symposium on AI in Government and Public Sector. arXiv:2105.06742.

Pavlik, J. A., Ludden, I. G., Jacobson, S. H., & Sewell, E. C. (2021). Airplane seating assignment problem.Service Science, 13(1), 1-18.

Pavlik, J. A., Sewell, E. C., & Jacobson, S. H. (2021). Two new bidirectional search algorithms. Computational Optimization and Applications, 80(2), 377-409.

Cobb, A., Jalaian, B., Bastian, N. & Russell, S. (2021). Toward Safe Decision-Making via Uncertainty Quantification in Machine Learning. In William Lawless, Ranjeev Mittu, Donald Sofge, Thomas Shortell and Tom McDermott (Eds.), Systems Engineering and Artificial Intelligence (pp. 379-399). Springer, Cham.

Alhajjar, E., Fameli, R. & Warren, S. (2021). Are Terrorist Networks Just Glorified Criminal Cells? Northeast Journal of Complex Systems, 3(1): 1-16.

Maxwell, P., Niblick, D. & Ruiz, D. (2021). Using Side Channel Information and Artificial Intelligence for Malware Detection. Proceedings of the 2021 IEEE International Conference on Artificial Intelligence and Computer Applications, pp. 408 - 413. IEEE.

Alhajjar, E., Maxwell, P. & Bastian, N. (2021). Adversarial Machine Learning in Network Intrusion Detection Systems. Expert Systems with Applications, 186(115782): 1-13.

Bastian, N. (2021). Artificial Intelligence for Defense Applications. Journal of Defense Modeling and Simulation, 18(3): 173-174.

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. (2021). Stacked Generalizations in Imbalanced Fraud Data Sets using Resampling Methods. Journal of Defense Modeling and Simulation. 18(3): 175-192.

2020

Schoenherr, J., Thomson, R., & Pyke, A. (2020). Integrating Ethical Sensemaking into Cybersecurity: A Problem-Based Approach. In 2020 SBP-BRiMS Annual Conference, Washington, DC.

Mitsopoulos, K., Somers, S., Lebiere, C., & Thomson, R. (2020). Cognitive Architectures for Introspecting Deep Reinforcement Learning Agents. In 2020 International Conference on Learning Representations (ICLR), Workshop in Bridging Artificial Intelligence and Cognitive Science.

Schoenherr, J. R., & Thomson, R. (2020). Beyond the Prisoner's Dilemma: the Social Dilemmas of Cybersecurity. In 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), Dublin, Ireland, 2020, pp. 1-7, doi: 10.1109/CyberSA49311.2020.9139644.

Schoenherr, J. R., & Thomson, R. (2020). Insider Threat Detection: A Solution in Search of a Problem. In 2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), Dublin, Ireland, 2020, pp. 1-7, doi: 10.1109/CyberSecurity49315.2020.9138862

Pyke, A., Schoenherr, J., & Thomson, R. (2020). A Professional Ethics Curriculum for Cyber Operations. In IEEE International Conference on Software Engineering Education & Training (CSEE&T 2020).

Milner, A., Seong, D. H., Brewer, R., Baker, A., Thomson, R., Krausman, A., Neubauer, C., Canady, J., Fitzhugh, S., Rovira, E., & Schaefer, K. E. (2020). Identifying New Team Trust and Team Cohesion Metrics that Support Future of Human-Autonomy Teams. In Applied Human Factors and Ergonomics - Special Section in Driving Simulation.

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.

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.

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