Identification should include
- intentional falsification of data to disrupt crop, livestock, or poultry sectors
- introduction of rogue data into a sensor network, which damages a crop, herd, flock, or process
- insufficiently vetted machine learning (ML) modeling.
Process/Skill Questions:
- What are man-in-the-middle, data-diddling, trust-relationship, and session-hijacking attacks?
- How could falsified data harm an agriculture business?
- How might a computer criminal attempt to falsely modify digital data?
- How might a farmer/producer be affected if a variable-rate application (VRA) sensor network for fertilizer was secretly programmed to deliver at a rate higher than appropriate?