IoT-Praetor: Undesired Behaviors Detection for IoT Devices
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
Due to insecure design and configuration, the Internet-of-Things (IoT) devices are vulnerable to various security issues. In most attacks against IoT, e.g., Mirai, attackers control devices to perform malicious behaviors that are not expected by owners and administrators. Therefore, how to effectively detect malicious behaviors is crucial to protect the security of IoT devices. Different from powerful PCs and servers, resource-constrained IoT devices are generally used to execute the specific function and their behaviors are limited. Based on this observation, we propose IoT-Praetor, an undesired behavior security detection system for IoT devices. In IoT-Praetor, a new device usage description (DUD) model is proposed to construct an IoT device behavior specification, including communication and interaction behaviors. Furthermore, automatic behavior extraction approaches are presented. We also design a behavior rule engine to detect device behaviors in real time. To evaluate the effectiveness of IoT-Praetor, we implemented our methods on Samsung SmartThings and performed a security test. The evaluation results show that the successful detection rate of malicious interaction behavior is 94.5% on average, and the detection rate of malicious communication behavior is above 98%, and system running time delay is only in millisecond level.
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