| Issue |
EPJ Web Conf.
Volume 360, 2026
1st International Conference on “Quantum Innovations for Computing and Knowledge Systems” (QUICK’26)
|
|
|---|---|---|
| Article Number | 01001 | |
| Number of page(s) | 15 | |
| DOI | https://doi.org/10.1051/epjconf/202636001001 | |
| Published online | 23 March 2026 | |
https://doi.org/10.1051/epjconf/202636001001
When Doing Nothing Is the Optimal Cyber Defense: Quantum-Inspired Abstention as a First-Class Security Action
RMIT University, Melbourne, VIC, Australia
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 23 March 2026
Abstract
Cyber security usually favors action: when an abnormality is detected, systems react visibly (block, alert, rotate) even when evidence is weak. However, work on abstention and selective prediction shows that deferring commitment can be rational under uncertainty, trading coverage for lower expected risk. At the same time, operational realities such as alert overload and adaptive adversaries imply that visible defensive reactions can backfire by increasing analyst burden and providing feedback that supports attacker probing and policy inference. This paper presents quantum-inspired abstention as a first-class security action. Using a quantum decision-theoretic lens, I model defensive commitment as a “measurement” that collapses an uncertain belief state into an externally observable response, and I define abstention as deliberate non-commitment that suppresses or delays measurement when uncertainty and leakage risk are high. I integrate these ideas into a conceptual framework and a minimal loss decomposition separating security loss, operational cost, and leakage-driven adversarial learning. I illustrate the approach through SOC triage and network intrusion detection scenarios, and I provide a lightweight simulation that instantiates the trade-offs among these losses—without requiring quantum hardware.
Key words: cyber defense / abstention / selective prediction / quantum-inspired / adversarial learning / information leakage
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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