Combating Informational Denial-of-Service (IDoS) Attacks: Modeling and Mitigation of Attentional Human Vulnerability
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Rapid sequences of prompts can induce cognitive overload, creating informational denial-of-service conditions.
“For example, rapid sequences of prompts can induce cognitive overload, creating informational denial-of-service conditions (Huang & Zhu, 2021).”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Huang & Zhu, 2021
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 12
- [2]2. C. D. Wickens, J. G. Hollands, S. Banbury, and R. Parasuraman, Engineering psychology and human performance . Psychology Press, 2015.
- [3]3. W. Casey, J. A. Morales, E. Wright, Q. Zhu, and B. Mishra, 'Compliance signaling games: toward modeling the deterrence of insider threats,' Computational and Mathematical Organization Theory , vol. 22, no. 3, pp. 318-349, 2016.
- [4]4. L. Huang and Q. Zhu, 'Duplicity games for deception design with an application to insider threat mitigation,' IEEE Transactions on Information Forensics and Security , 2021.
- [5]5. --, 'Inadvert: An interactive and adaptive counterdeception platform for attention enhancement and phishing prevention,' arXiv preprint arXiv:2106.06907 , 2021. · arXiv
- [6]6. Y. Huang, L. Huang, and Q. Zhu, 'Reinforcement learning for feedback-enabled cyber resilience,' arXiv preprint arXiv:2107.00783 , 2021. · arXiv
- [7]7. L. Huang and Q. Zhu, 'A dynamic games approach to proactive defense strategies against advanced persistent threats in cyber-physical systems,' Computers & Security , vol. 89, p. 101660, 2020.
- [8]8. --, 'A dynamic game framework for rational and persistent robot deception with an application to deceptive pursuit-evasion,' IEEE Transactions on Automation Science and Engineering , pp. 1-15, 2021.
- [9]9. --, 'Adaptive honeypot engagement through reinforcement learning of semi-markov decision processes,' in International Conference on Decision and Game Theory for Security . Springer, 2019, pp. 196-216.
- [10]10. D. Zhao, J. Liu, J. Wang, W. Niu, E. Tong, T. Chen, and G. Li, 'Bidirectional rnn-based few-shot training for detecting multi-stage attack,' arXiv preprint arXiv:1905.03454 , 2019. · arXiv
- [11]11. C. A. Sims, 'Implications of rational inattention,' Journal of monetary Economics , vol. 50, no. 3, pp. 665-690, 2003.
- [14]14. T. Ban, N. Samuel, T. Takahashi, and D. Inoue, 'Combat security alert fatigue with aiassisted techniques,' in Cyber Security Experimentation and Test Workshop , 2021, pp. 9-16.
- [15]15. D. Bertsekas and J. Tsitsiklis, Neuro-Dynamic Programming , 01 1996, vol. 27.
Cited by
Sorted by citations
Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents
de Witt, C. S., Krawiecka, K., Krawczuk, I., Hagag, B., Anderson, W. L., Belcak, P., Bucknall, B., Cai, X., Chopra, A., Cohen, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.02077 · Cited by 2 · see citing works
- Authors
- Huang, L., & Zhu, Q.
- Affiliations
- New York University (US)
- Corresponding author
- Huang, Linan
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 314-333
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3193915010
- Cited by
- 8
- References
- 0
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 3 · 2021: 1
- FWCI
- 1.66
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Keywords
- human attention, cognitive depletion, semi-Markov processes, alert management, dynamic programming, online learning, risk mitigation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review