Automated Vulnerability Discovery and Exploitation in the Internet of Things
Bibliographic record
Abstract
Recently, automated software vulnerability detection and exploitation in Internet of Things (IoT) has attracted more and more attention, due to IoT’s fast adoption and high social impact. However, the task is challenging and the solutions are non-trivial: the existing methods have limited effectiveness at discovering vulnerabilities capable of compromising IoT systems. To address this, we propose an Automated Vulnerability Discovery and Exploitation framework with a Scheduling strategy, AutoDES that aims to improve the efficiency and effectiveness of vulnerability discovery and exploitation. In the vulnerability discovery stage, we use our Anti-Driller technique to mitigate the “path explosion” problem. This approach first generates a specific input proceeding from symbolic execution based on a Control Flow Graph (CFG). It then leverages a mutation-based fuzzer to find vulnerabilities while avoiding invalid mutations. In the vulnerability exploitation stage, we analyze the characteristics of vulnerabilities and then propose to generate exploits, via the use of several proposed attack techniques that can produce a shell based on the detected vulnerabilities. We also propose a genetic algorithm (GA)-based scheduling strategy (AutoS) that helps with assigning the computing resources dynamically and efficiently. The extensive experimental results on the RHG 2018 challenge dataset and the BCTF-RHG 2019 challenge dataset clearly demonstrate the effectiveness and efficiency of the proposed framework.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".