ForenThings: An Interactive Framework for Crime Scene Reconstruction in IoT Forensics
Bibliographic record
Abstract
In IoT platforms, devices and sensors can interact with each other via smart apps that utilize automation settings preconfigured by users, resulting in significant amounts of potential forensic data. Existing IoT forensic approaches can pinpoint relevant data sources for specific activities in smart environments using static code analysis and instrumentation techniques. However, recent IoT platforms like SmartThings no longer run application code on their infrastructure, making access to source code impossible for existing IoT forensic solutions. To bridge this gap, this paper introduces ForenThings , an interactive framework for crime scene reconstruction in smart environments. The main idea is to convert each IoT device and smart app to a responsive agent, enabling them to participate in a forensic investigation of a security incident collaboratively. Instead of relying on static code analysis or instrumentation, ForenThings reconstructs the scene from the device and app events forwarded by the IoT platform. We develop a ForenThings prototype for the SmartThings platform and test its effectiveness for both normal scenarios and 12 real-world IoT attack scenarios. The evaluation shows that ForenThings can achieve 100% data provenance coverage in reconstructing various crime scenes in a smart environment with negligible runtime and resource overhead.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".