ForenThings: An Interactive Framework for Crime Scene Reconstruction in IoT Forensics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it