oHIT - A framework for openHAB Interaction Threats Identification in IoT Systems
Bibliographic record
Abstract
As we increase our reliance upon Internet of Things (IoT) systems, we increase our convenience and efficiency, but we also expose ourselves to significant safety challenges, particularly in home automation platforms like openHAB. openHAB’s rule-based trigger-condition-action (TCA) paradigm allows for extensive flexibility in designing one’s home system, but increases the risk of unintended rule interactions, leading to unpredictable or unsafe behaviors. This paper presents oHIT, a novel framework for detecting Rule Interaction Threats in openHAB systems. oHIT systematically analyzes openHAB rules files, identifies foundational threat categories — including Action Contradiction, Trigger Cascade, and Condition Cascade — and differentiates between strong and weak variations of these threats. The framework leverages static analysis, transformation techniques, and symbolic reasoningto enable efficient threat detection. Evaluations show that oHIT achieves overall precision of 79% with a recall of 100% in detecting Rule Interaction threats on two real-world datasets, demonstrating oHIT’s effectiveness. This research addresses a critical gap in IoT safety analysis, paving the way for safer and more reliable automation in interconnected environments.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".