mlsanityData to 7 Oct 2026

CyPhERS: A Cyber-Physical Event Reasoning System providing real-time situational awareness for attack and fault response

arXiv 2305.16907 ↗First posted 26 May 2023cs.CR cs.SY eess.SPcited by 0

Not on the map. Why?

Abstract (arXiv)

Cyber-physical systems (CPSs) constitute the backbone of critical infrastructures such as power grids or water distribution networks. Operating failures in these systems can cause serious risks for society. To avoid or minimize downtime, operators require real-time awareness about critical incidents. However, online event identification in CPSs is challenged by the complex interdependency of numerous physical and digital components, requiring to take cyber attacks and physical failures equally into account. The online event identification problem is further complicated through the lack of historical observations of critical but rare events, and the continuous evolution of cyber attack strategies. This work introduces and demonstrates CyPhERS, a Cyber-Physical Event Reasoning System. CyPhERS provides real-time information pertaining the occurrence, location, physical impact, and root cause of potentially critical events in CPSs, without the need for historical event observations. Key novelty of CyPhERS is the capability to generate informative and interpretable event signatures of known and unknown types of both cyber attacks and physical failures. The concept is evaluated and benchmarked on a demonstration case that comprises a multitude of attack and fault events targeting various components of a CPS. The results demonstrate that the event signatures provide relevant and inferable information on both known and unknown event types.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 54 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

14 of 54 references.

References · 14

  1. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  2. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  3. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 181
  4. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  5. 2021Darts: User-Friendly Modern Machine Learning for Time Seriescited by 90
  6. 2014Consistency of random forestscited by 84
  7. 2020Kaggle forecasting competitions: An overlooked learning opportunitycited by 55
  8. 2017Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Datacited by 47
  9. 2021Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filteringcited by 31
  10. 2021A Survey on Anomaly Detection for Technical Systems using LSTM Networkscited by 27
  11. 2018Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learningcited by 8
  12. 2021HURRA! Human readable router anomaly detectioncited by 5
2 more
  1. 2019RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Seriescited by 1
  2. 2022Assessment of Cyber-Physical Intrusion Detection and Classification for Industrial Control Systemscited by 1

Built on by

Not cited yet.