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RESEARCH LAB OVERVIEW

The Digital Force Protection (DFP) team serves as the integrator of civilian and military research, addressing force protection and operational security concerns for both units and individuals, with a particular focus on commercial surveillance and the digital economy. We leverage partnerships with military and civilian researchers, lawmakers, and practitioners, transcending traditional boundaries to synthesize interdisciplinary research. This collaborative effort informs Army decisions and shapes emerging policy and doctrine, supporting a comprehensive national approach to privacy and security.

The widespread collection of commercial data in every facet of daily life poses significant national security risks. Ubiquitous Technical Surveillance (UTS) refers to the continuous data gathering from sensors embedded in websites, smartphone applications, home appliances, vehicles, and more. This data, which can include geolocation, personally identifiable information (PII), personal health information (PHI), preferences, and religious beliefs, is often utilized for marketing within the broader “surveillance economy.” However, it can also be exploited to target individuals or organizations.

To navigate this evolving landscape, our team conducts interdisciplinary research and provides advisement to the Army on matters of online force protection, privacy, commercial surveillance, and the Army’s emerging Information Advantage doctrine.

DFP has several research focus areas, including:

  • Commercial Surveillance, Ubiquitous Technical Surveillance, and the Erosion of Privacy: This research area focuses on assessing and quantifying risks associated with data brokers, evaluating privacy concerns across a wide range of devices, and identifying vulnerabilities in contracting language. The erosion of privacy through commercial entities is unprecedented today. Devices such as smartphones, smart home appliances, wearable technology, vehicles, and even personal assistants like smart speakers continuously collect data, including location information, personal health data, and other sensitive details. This constant data collection, combined with the global nature of data flows that ignore national boundaries, creates significant challenges in understanding and mitigating risks within the information domain. Our research aims to evaluate these risks, particularly how they affect privacy, and to develop strategies to protect against them.
  • Information Advantage: Our team supports the Cyber Center of Excellence’s Information Advantage Campaign of Learning, including providing warfighter assessment support to enhance operational capabilities.
  • Data privacy and National Security Risks: understand how the commercial surveillance economy and commercial data collection poses risks to individuals and the force. Assess privacy enhancing technology and other best practices across different academic disciplines.
  • Narrative Warfare: Understanding the tactics, techniques and processes that malign actors leverage culturally significant stories to elicit an emotional response and behavioral change. Information Advantage Campaign of Learning – understanding what problem the Army is trying to solve with Information Advantage and help shape the future implementation of DOTML-PFP solutions.
  • Historic and Legal Aspects of Information Warfare: The intersection of the history of cyber espionage, adversarial tactics, techniques, and procedures, with social media the extent to which information manipulation amounts to an unlawful intervention into the affairs of another State (nation) in violation of international law.

DFP has several research focus areas, including:

  • Commercial Data Report
  • Vehicle Privacy Project
  • Social Media & Influence
  • Smartphone App Research
  • Biometrics
  • AdTech, Microtargeting, and Commercial Surveillance
Vehicle Privacy Vehicle Privacy
PUBLICATIONS, REPORTS, AND PRESENTATIONS

2024

Chojnacki, B. (2024). Enhancing recruiting efforts for the U.S. Army: A study of immersive virtual reality experiences. Monterey, CA: Naval Postgraduate School.

Dawson, J., & Matthew, K. (2024). Data as ammunition: A new framework for information warfare. The Cyber Defense Review, 9(2), 97-99.

Fox, J. 2024. The New Insider Threat: How Commercially Available Data can be used to Target and Persuade. The Managing Insider Risk & Organizational Resilience (MIROR) Journal, (2)1, p. 61-86.

Harrell, N., Cruickshank, I., Master, A. 2024. Overcoming Social Media API Restrictions: Building an Effective Web Scraper. International Conference on Web and Social Media (ICWSM) Workshop Proceedings. DOI: 10.36190/2024.72.

Master, A. 2024. AdTech and Military-Friendly Banks: Data Vulnerabilities for Servicemembers, Families, and Veterans. AvengerCon VIII Conference, Presentation.

Schoenherr, J. R., & Thomson, R. (2024, January). When AI Fails, Who Do We Blame? Attributing Responsibility in Human-AI Interactions. IEEE Transactions on Technology and Society.

Stocco, A., Rice, P., Thomson, R., Smith, B., Morrison, D., & Lebiere, C. (2024, January). An integrated computational framework for the neurobiology of memory based on the ACT-R declarative memory system. Computational Brain & Behavior, 7(1), 129-149.

Thomson, R., & Lebiere, C. (2024, September). Comparing Similarity and Homophily-Based Cognitive Models of Influence and Conformity. In International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (pp. 47-57). Cham: Springer Nature Switzerland.

Thomson, R., & Frangia, W. (2024, October). Investigating the use of belief-bias to measure acceptance of false information. Computational and Mathematical Organization Theory, 1-19.

Thomson, R., Hariharan, A., Renshaw, S., Al-khateeb, S., Burger, A., Park, P., & Pyke, A. (Eds.). (2024, September). Social, Cultural, and Behavioral Modeling: 17th International Conference, SBP-BRiMS 2024, Pittsburgh, PA, USA, September 18-20, 2024, Proceedings (Vol. 14972). Springer Nature.

Thomson, R., Cassenti, D. N., & Hawkins, T. (2024, October). Too much of a good thing: How varying levels of automation impact user performance in a simulated intrusion detection task. Computers in Human Behavior Reports, 16, 100511.

Thomson, R. H., Cranford, E. A., Tucker, G., & Lebiere, C. (2024, June). Comparison of cognitively-inspired salience and feature importance techniques in intrusion detection datasets. In Assurance and Security for AI-enabled Systems (Vol. 13054, pp. 186-196). SPIE.

Thomson, R., Cranford, E., Somers, S., & Lebiere, C. (2024, January). A novel approach to intrusion detection using a cognitively-inspired algorithm. In Proceedings of the 57th Hawaii International Conference on System Sciences.

AFFILIATED RESEARCHERS
  • LTC Jessica Dawson, Ph.D.

    Division Chief, Digital Force Protection
  • LTC Bruce Chojnacki

    Research Scientist, Digital Force Protection
  • MAJ Alex Master, Ph.D.

    Research Scientist, Digital Force Protection
  • Dr. Robert Thomson

    Senior Research Scientist
PROJECT SPONSORS