Securing smart dairy farms: A cybersecurity analysis of IoT-based cow health monitoring systems
Bibliographic record
Abstract
Internet of Things (IoT) devices are proliferating in the agricultural sector as more and more farmers depend on these tools to manage their operations. From completely autonomous milking parlors to crop management and greenhouse automation technology is being used in an unprecedented variety of ways to improve productivity. The advantages are enormous, allowing farms to grow to accommodate more resources (such as water) and less need for human labor; in fact, in some areas, it is now economically impossible for farmers to return to more conventional methods of production. Regrettably, the risk of cyber assaults has also increased alongside technological advancements. The safety of the food supply as a whole and specific farm may be jeopardized if any one of them were to be successful. Collars used to track cow health in an intelligent dairy farm are thoroughly examined in this study. To our knowledge, this is the first study of its kind to focus on the cyber defences of actual farm equipment. We were able to effectively decipher the wireless protocol and show that we could trick the system into thinking we were one of the sensors by injecting bogus data. According to the results of the tests, the data endpoint software and the system that receives signals from the sensors are both susceptible to data injection. In this study, we focus on the vulnerabilities that pose the greatest risk and propose some solutions that could be built into sensors down the road.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".