# A Review of Cybersecurity Incidents in the Food and Agriculture Sector

arXiv: https://arxiv.org/abs/2403.08036 · HTML: https://mlsanity.com/paper/2403.08036/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 208 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [Human-Centered Artificial Intelligence and Machine Learning](https://mlsanity.com/paper/1901.11184/) (2019)
- [weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming](https://mlsanity.com/paper/1709.03329/) (2017)
- [A Survey on AI Assurance](https://mlsanity.com/paper/2111.07505/) (2021)
- [Foundations of data imbalance and solutions for a data democracy](https://mlsanity.com/paper/2108.00071/) (2021)
- [Cybersecurity of Industrial Cyber-Physical Systems: A Review](https://mlsanity.com/paper/2101.03564/) (2021)
- [A Review of Cybersecurity Incidents in the Water Sector](https://mlsanity.com/paper/2001.11144/) (2020)
- [DeepAg: Deep Learning Approach for Measuring the Effects of Outlier Events on Agricultural Production and Policy](https://mlsanity.com/paper/2110.12062/) (2021)
- [ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems](https://mlsanity.com/paper/2310.17654/) (2023)
- [Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning](https://mlsanity.com/paper/2111.07508/) (2021)

## Built on by (most cited first, 1 of 1)

- [Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions](https://mlsanity.com/paper/2508.08326/) (2025)

## Abstract (arXiv)

The increasing utilization of emerging technologies in the Food & Agriculture (FA) sector has heightened the need for security to minimize cyber risks. Considering this aspect, this manuscript reviews disclosed and documented cybersecurity incidents in the FA sector. For this purpose, thirty cybersecurity incidents were identified, which took place between July 2011 and April 2023. The details of these incidents are reported from multiple sources such as: the private industry and flash notifications generated by the Federal Bureau of Investigation (FBI), internal reports from the affected organizations, and available media sources. Considering the available information, a brief description of the security threat, ransom amount, and impact on the organization are discussed for each incident. This review reports an increased frequency of cybersecurity threats to the FA sector. To minimize these cyber risks, popular cybersecurity frameworks and recent agriculture-specific cybersecurity solutions are also discussed. Further, the need for AI assurance in the FA sector is explained, and the Farmer-Centered AI (FCAI) framework is proposed. The main aim of the FCAI framework is to support farmers in decision-making for agricultural production, by incorporating AI assurance. Lastly, the effects of the reported cyber incidents on other critical infrastructures, food security, and the economy are noted, along with specifying the open issues for future development.