Traditional IOCs Meet Dynamic App–Device Interactions for IoT-Specific Threat Intelligence
Bibliographic record
Abstract
While enjoying widespread popularity, IoT faces numerous threats using both the traditional (e.g., common vulnerabilities and exposures (CVEs) and common weakness enumerations (CWEs)) and IoT-specific (e.g., device-application interactions) attack vectors. Therefore, gathering threat intelligence for an IoT environment is equally essential if not more (compared to many other IT environments). However, extracting threat intelligence from an IoT deployment poses several unique challenges. First, most IoT implementations are not logging threat-related information and even if they are, their logging mechanisms require significant additional effort to turn those logs to a threat intelligence. Second, there is no clear definition of Indicators of Compromise (IOCs), which are the key inputs to threat intelligence, in the context of IoT; including how to combine IoT-specific IOCs, including that are involved with the dynamic app–device interactions. In this article, we propose IoTINT, a solution to obtain IoT-specific threat intelligence while addressing the above-mentioned challenges. Specifically, our key ideas are to first enable logging in IoT devices and apps without requiring any code instrumentation (in contrast to the existing approaches), then iteratively finding dynamic interactions between the IoT devices and their apps that are defined by the automation rules and result in various security threats, and finally, combine both the app–device interactions with traditional IOCs (such as CVEs and CWEs) to build a comprehensive threat intelligence for IoT. We implement IoTINT for the Samsung SmartThings, a major smart home platform, and evaluate its performance (e.g., 100% coverage in extracting threat intelligence within 11 s for ten realistic IoT attack scenarios).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".