Arming IDS Researchers with a Robotic Arm Dataset
Bibliographic record
Abstract
Industry 4.0 is rapidly transforming traditional manufacturing practices. Smart manufacturing technologies that automate research and development using a combination of robotic arms and domain-specific cyber-physical systems are at the core of this transformation. Unfortunately, dependence on networked communication increases the risk of security attacks, which must be mitigated using either platforms that are secure by design or intrusion detection and prevention systems. We report on an ongoing project to design and develop intrusion detection systems (IDS) for the Hein Lab, a smart manufacturing research lab in the chemical sciences domain. Designing effective IDS requires large datasets and high-quality, domain-specific benchmarks, which are difficult to obtain. To address this gap, we present the Robotic Arm Dataset (RAD), which we collected at the Hein Lab over a three-month period. We also present our non-intrusive tracing framework RATracer, which can be retrofitted onto any existing Python-based automation pipeline, and two sets of preliminary analyses based on the command and power data in RAD.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".