Dynamic Decision Support Framework for Smart Home Security
Bibliographic record
Abstract
Smart homes face evolving cybersecurity threats, requiring adaptive defense strategies. Existing frameworks for defending smart homes provide static optimization approaches for security controls that fail to address the dynamic nature of threats and often overlook usability factors impacting user adoption. This work presents a framework that outperforms static security optimization approaches, reducing successful attacks by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{7 0 - 7 8 \%}$</tex> across a diverse user profile portfolio while maintaining acceptable usability levels. The framework uses time-dependent modeling that adapts to evolving threats and varying user preferences. We incorporate realistic dynamics of smart home security by modeling attack patterns with varying temporal characteristics and security control effectiveness. The framework quantifies usability impacts through user satisfaction, technical capability, and compliance rates. We experiment across six user profiles (security-focused, usability-focused, balanced, elderly, tech-enthusiast, and family home) to demonstrate that the framework consistently finds optimal security-usability tradeoff positions compared to static optimization. For most profiles, the framework improvement in overall reward metrics despite moderate decreases in user satisfaction, showcasing its ability to balance security with usability constraints. The framework is a significant advancement in smart home security that provides adequate protection and maintains high security standards while preserving essential usability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".