Tipping the scales: the attribution problem and the feasibility of deterrence against cyberattack
Bibliographic record
Abstract
Cyber attackers rely on deception to exploit vulnerabilities and obfuscate their identity, which makes many pessimistic about cyber deterrence. The attribution problem appears to make retaliatory punishment, contrasted with defensive denial, particularly ineffective. Yet observable deterrence failures against targets of lower value tell us little about the ability to deter attacks against higher value targets, where defenders may be more willing and able to pay the costs of attribution and punishment. Counterintuitively, costs of attribution and response may decline with scale. Reliance on deception is a double-edged sword that provides some advantages to the attacker but undermines offensive coercion and creates risks for ambitious intruders. Many of the properties of cybersecurity assumed to be determined by technology, such as the advantage of offense over defense, the difficulty of attribution, and the inefficacy of deterrence, are in fact consequences of political factors like the value of the target and the scale-dependent costs of exploitation and retaliation. Assumptions about attribution can be incorporated into traditional international relations concepts of uncertainty and credibility, even as attribution involves uncertainty about the identity of the opponent, not just interests and capabilities. This article uses a formal model to explain why there are many low-value anonymous attacks but few high-value ones, showing how different assumptions about the scaling of exploitation and retaliation costs lead to different degrees of coverage and effectiveness for deterrence by denial and punishment. Deterrence works where it is needed most, yet it usually fails everywhere else.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".