Cited by 13Adversarial Robustness in Machine LearningInformation and Cyber SecurityNetwork Security and Intrusion Detection

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Retrieval Augmented Generation for Robust Cyber Defense

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Pacific Northwest National Laboratory (PNNL), Richland, WA (United States), Rahman, Moqsadur, USDOE Laboratory Directed Research and Development (LDRD) Program, Piryani, Krish, Sanchez, Aaron, Munikoti, Sai, De La Torre, Luis, Levin, Maxwell, Akbar, Monika, Hossain, Mahmud, Hasan, Monowar, Halappanavar, Mahantesh·2024

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Cited by 13

Research works printed but not linked: 7

  1. [1]1. S Das, E. Serra, M. Halappanavar, A. Pothen, and E. Al-Shaer. "V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities." In proceedings of the 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA). Porto, Portugal. October 2021. [Best Application Paper Award]
  2. [2]2. "VWC-BERT: Scaling Vulnerability-Weakness-Exploit Mapping 59 60 3 on Modern AI Accelerators." In IEEE International Conference on Big Data (IEEE BigData 2022) December 17-20, 2022. Osaka, Japan.
  3. [5]5. U Bhatia, S Chatterjee, A Ganguly, M Halappanavar, R Tipireddy, and R Brigantic. "Aviation Transportation, Cyber Threats, and Network-of-Networks: Conceptual Framing and Modeling Perspectives for Translating Theory to Practice." Accepted for publication in proceedings of the IEEE International Symposium on Technologies for Homeland Security (HST).
  4. [6]6. "FlipNet: Modeling Covert and Persistent Attacks on Networked Resources," 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, 2017, pp. 2444-2451.
  5. [7]7. TH Bhuiyan, A Nandi, H Medal, and M Halappanavar. "Minimizing Expected Maximum Risk from Cyber-Attacks with Probabilistic Attack Success." In proceedings of the IEEE International Conference on Technologies for Homeland Security. May 10 -12, 2016. Waltham, Massachusetts, USA.
  6. [8]8. "Identifying Vulnerabilities and Hardening Attack Graphs for Networked Systems." In proceedings of the IEEE International Conference on Technologies for Homeland Security. May 10 -12, 2016. Waltham, Massachusetts, USA.
  7. [9]9. P Ramuhalli, M Halappanavar, J Coble, M Dixit. "Towards A Theory of Autonomous Reconstitution of Compromised Cyber-Systems." In proceedings of the 13th Annual IEEE Conference on Technologies for Homeland Security, Waltham, MA, 12 --14 Nov, 2013. [Best Paper Award in Cyber Security Track]

Metadata

Authors
Pacific Northwest National Laboratory (PNNL), R., Rahman, M., Program, U. L. D. R. A. D. (., Piryani, K., Sanchez, A., Munikoti, S., De La Torre, L., Levin, M., Akbar, M., Hossain, M., Hasan, M., & Halappanavar, M.
Affiliations
  • Pacific Northwest National Laboratory (US)
  • Washington State Univ. Tri-Cities, Richland, WA (United States)
  • The University of Texas at El Paso (US)
  • Washington State University (US)
Published
2024-09-30
OpenAlex
W4403854698
Cited by
13
References
0
Citations by year
2026: 8 · 2025: 5
Type
report
Language
en
Open access
Locations
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
  • USDOE Laboratory Directed Research and Development (LDRD) Program
  • DOE (AC05-76RL01830)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Keywords
retrieval-augmented generation, cyber defense, large language models, robustness

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CoChat. (2026). Citation check: Retrieval Augmented Generation for Robust Cyber Defense. https://app.cochat.ai/papers/10.2172/2474934
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Pacific Northwest National Laboratory (PNNL), R., Rahman, M., Program, U. L. D. R. A. D. (., Piryani, K., Sanchez, A., Munikoti, S., De La Torre, L., Levin, M., Akbar, M., Hossain, M., Hasan, M., & Halappanavar, M. (2024). Retrieval Augmented Generation for Robust Cyber Defense. https://doi.org/10.2172/2474934