Causal Discovery-based Online Unsupervised Anomaly Detection for Cyber-Physical Systems

Core Concepts

Causal discovery can be used to efficiently learn a normal causal model of a cyber-physical system, and online monitoring of causal link persistence can enable prompt detection and identification of anomalies.

Abstract

The paper proposes a novel approach for online unsupervised anomaly detection (OUAD) in cyber-physical systems (CPSs) using causal discovery. The key ideas are: Causal discovery: The authors use the PCMCI algorithm to learn a normal causal model of the CPS from time series data of the system under normal operation. This causal model captures the directed causal relationships between different variables in the system. Online anomaly detection: During real-time operation, the authors monitor the persistence of the causal links learned from the normal model. When the causal relationships between variables deviate significantly from the normal model, it indicates the presence of an anomaly. The authors use a threshold-based approach to identify broken causal links and raise anomaly alarms. Explainability: By analyzing the specific causal links that are broken during an anomaly, the authors can identify the potential sources of the anomaly, providing better explainability compared to black-box neural network models. The authors validate their approach on two benchmark CPS datasets - the SWAT industrial water treatment plant and the Pepper social robot. They show that their causal discovery-based method achieves higher training efficiency, outperforms state-of-the-art neural network models in anomaly detection accuracy, and correctly identifies the sources of over 10 different anomalies.

Stats

The SWAT dataset contains 51 physical variables representing actuators and sensors in a 6-stage water filtration process. The Pepper dataset contains 256 sensor readings from a humanoid robot, including joint kinematics, accelerometers, and LED/laser configurations.

Quotes

"Online unsupervised detection of anomalies is crucial to guarantee the correct operation of cyber-physical systems and the safety of humans interacting with them." "We use causal discovery to learn a normal causal graph of the system, and we evaluate the persistency of causal links during real-time acquisition of sensor data to promptly detect anomalies."

Key Insights Distilled From

Explainable Online Unsupervised Anomaly Detection for Cyber-Physical Systems via Causal Discovery from Time Series

by Daniele Meli at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09871.pdf

Deeper Inquiries

How can the causal discovery process be further improved to handle more complex, nonlinear relationships between variables in the CPS

To enhance the causal discovery process for handling more complex, nonlinear relationships between variables in Cyber-Physical Systems (CPS), several improvements can be implemented: Nonlinear Causal Models: Incorporating nonlinear causal models, such as nonparametric methods like kernel-based approaches or neural networks, can capture intricate relationships that linear models might miss. Feature Engineering: Utilizing advanced feature engineering techniques like time-delay embedding or kernel methods can help represent the data in a higher-dimensional space, enabling the detection of nonlinear causal relationships. Ensemble Methods: Employing ensemble methods that combine multiple causal discovery algorithms or models can provide a more comprehensive understanding of the complex causal structures within CPS. Dynamic Bayesian Networks: Implementing Dynamic Bayesian Networks can capture the time-varying nature of causal relationships in CPS, allowing for the modeling of evolving dependencies.

What are the potential limitations of the linear assumption used in the online anomaly detection phase, and how can they be addressed

The linear assumption in the online anomaly detection phase may have limitations in capturing the full complexity of causal relationships in CPS. Some potential drawbacks include: Limited Representation: Linear models may not adequately represent the intricate nonlinear interactions between variables in CPS, leading to inaccuracies in anomaly detection. Sensitivity to Outliers: Linear models are sensitive to outliers, which can skew the estimation of causal relationships and impact the detection of anomalies. Complexity Handling: Nonlinear relationships in CPS may not be effectively captured by linear models, potentially leading to missed anomalies or false alarms. To address these limitations, techniques such as: Nonlinear Causal Models: Transitioning to nonlinear causal models can better capture the complex relationships present in CPS data. Robust Estimation Methods: Implementing robust estimation techniques can mitigate the impact of outliers on causal inference. Adaptive Thresholding: Employing adaptive thresholding techniques that adjust to the varying complexity of causal relationships can enhance anomaly detection accuracy.

Can the causal discovery-based approach be extended to handle concept drift and evolving causal structures over time in the CPS

Extending the causal discovery-based approach to handle concept drift and evolving causal structures over time in CPS involves several strategies: Incremental Learning: Implementing incremental learning techniques that adapt the causal model as new data arrives can accommodate concept drift and evolving causal structures. Online Causal Inference: Utilizing online causal inference methods that continuously update causal relationships based on incoming data streams can capture real-time changes in the CPS. Temporal Causal Models: Developing temporal causal models that account for the time-dependent evolution of causal structures can effectively handle concept drift and evolving relationships. Adaptive Algorithms: Employing adaptive causal discovery algorithms that adjust their parameters based on changing data patterns can ensure the model's relevance in dynamic CPS environments.