# On the Elements of Datasets for Cyber Physical Systems Security

arXiv: https://arxiv.org/abs/2208.08255 · HTML: https://mlsanity.com/paper/2208.08255/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [A Survey on Industrial Control System Testbeds and Datasets for Security Research](https://mlsanity.com/paper/2102.05631/) (2021)
- [Analyzing the Usefulness of the DARPA OpTC Dataset in Cyber Threat Detection Research](https://mlsanity.com/paper/2103.03080/) (2021)
- [An Attacker Modeling Framework for the Assessment of Cyber-Physical Systems Security](https://mlsanity.com/paper/2006.03930/) (2020)
- [Automated Malware Design for Cyber Physical Systems](https://mlsanity.com/paper/2107.02538/) (2021)

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

- [PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphs](https://mlsanity.com/paper/2605.00929/) (2026)

## Abstract (arXiv)

Datasets are essential to apply AI algorithms to Cyber Physical System (CPS) Security. Due to scarcity of real CPS datasets, researchers elected to generate their own datasets using either real or virtualized testbeds. However, unlike other AI domains, a CPS is a complex system with many interfaces that determine its behavior. A dataset that comprises merely a collection of sensor measurements and network traffic may not be sufficient to develop resilient AI defensive or offensive agents. In this paper, we study the elements of CPS security datasets required to capture the system behavior and interactions, and propose a dataset architecture that has the potential to enhance the performance of AI algorithms in securing cyber physical systems. The framework includes dataset elements, attack representation, and required dataset features. We compare existing datasets to the proposed architecture to identify the current limitations and discuss the future of CPS dataset generation using testbeds.