Design-Level and Code-Level Security Analysis of IoT Devices
Bibliographic record
Abstract
The Internet of Things (IoT) is playing an important role in different aspects of our lives. Smart grids, smart cars, and medical devices all incorporate IoT devices as key components. The ubiquity and criticality of these devices make them an attractive target for attackers. Therefore, we need techniques to analyze their security so that we can address their potential vulnerabilities. IoT devices, unlike remote servers, are user-facing and, therefore, an attacker may interact with them more extensively, e.g., via physical access. Existing techniques for analyzing security of IoT devices either rely on a pre-defined set of attacks and, therefore, have limited effect or do not consider the specific capabilities the attackers have against IoT devices. Security analysis techniques may operate at the design-level, leveraging abstraction to avoid state-space explosion, or at the code-level for ensuring accuracy. In this article, we introduce two techniques, one at the design-level, and the other at the code-level, to analyze security of IoT devices, and compare their effectiveness. The former technique uses model checking, while the latter uses symbolic execution, to find attacks based on the attacker’s capabilities. We evaluate our techniques on an open source smart meter. We find that our code-level analysis technique is able to find three times more attacks and complete the analysis in half the time, compared to the design-level analysis technique, with no false positives.
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.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".