mlsanityData to 14 Oct 2026

Contextualized AI for Cyber Defense: An Automated Survey using LLMs

arXiv 2409.13524 ↗First posted 20 Sep 2024cs.CR cs.AIcited by 1

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Abstract (arXiv)

This paper surveys the potential of contextualized AI in enhancing cyber defense capabilities, revealing significant research growth from 2015 to 2024. We identify a focus on robustness, reliability, and integration methods, while noting gaps in organizational trust and governance frameworks. Our study employs two LLM-assisted literature survey methodologies: (A) ChatGPT 4 for exploration, and (B) Gemma 2:9b for filtering with Claude 3.5 Sonnet for full-text analysis. We discuss the effectiveness and challenges of using LLMs in academic research, providing insights for future researchers.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 104 references that are in the map. First posted on arXiv 20 Sep 2024.

Builds on

24 of 104 references.

References · 24

  1. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  2. 2020Extracting Training Data from Large Language Modelscited by 2,189
  3. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,055
  4. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 729
  5. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  6. 2023Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Modelscited by 154
  7. 2023From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacycited by 143
  8. 2021Examining Zero-Shot Vulnerability Repair with Large Language Modelscited by 105
  9. 2024LitLLM: A Toolkit for Scientific Literature Reviewcited by 63
  10. 2022Designing for Responsible Trust in AI Systems: A Communication Perspectivecited by 48
  11. 2021Generating Fake Cyber Threat Intelligence Using Transformer-Based Modelscited by 29
  12. 2023A Systematic Review on Fostering Appropriate Trust in Human-AI Interactioncited by 21
12 more
  1. 2023Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Modelscited by 21
  2. 2022SoK: Explainable Machine Learning for Computer Security Applicationscited by 20
  3. 2021Statistical Perspectives on Reliability of Artificial Intelligence Systemscited by 10
  4. 2023Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomescited by 9
  5. 2024Review of Generative AI Methods in Cybersecuritycited by 9
  6. 2024From COBIT to ISO 42001: Evaluating Cybersecurity Frameworks for Opportunities, Risks, and Regulatory Compliance in Commercializing Large Language Modelscited by 7
  7. 2021Reliability Analysis of Artificial Intelligence Systems Using Recurrent Events Data from Autonomous Vehiclescited by 6
  8. 2024Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviewscited by 6
  9. 2022AI for Beyond 5G Networks: A Cyber-Security Defense or Offense Enabler?cited by 5
  10. 2024LLAssist: Simple Tools for Automating Literature Review Using Large Language Modelscited by 4
  11. 2023AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecuritycited by 3
  12. 2024SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interestcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Anticipating Adversary Behavior in DevSecOps Scenarios through Large Language Modelscited by 0