Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems (2504.06320v1)

Abstract: Cyberattacks on critical infrastructure, particularly water distribution systems, have increased due to rapid digitalization and the integration of IoT devices and industrial control systems (ICS). These cyber-physical systems (CPS) introduce new vulnerabilities, requiring robust and automated intrusion detection systems (IDS) to mitigate potential threats. This study addresses key challenges in anomaly detection by leveraging time correlations in sensor data, integrating physical principles into machine learning models, and optimizing computational efficiency for edge applications. We build upon the concept of temporal differential consistency (TDC) loss to capture the dynamics of the system, ensuring meaningful relationships between dynamic states. Expanding on this foundation, we propose a hybrid autoencoder-based approach, referred to as hybrid TDC-AE, which extends TDC by incorporating both deterministic nodes and conventional statistical nodes. This hybrid structure enables the model to account for non-deterministic processes. Our approach achieves state-of-the-art classification performance while improving time to detect anomalies by 3%, outperforming the BATADAL challenge leader without requiring domain-specific knowledge, making it broadly applicable. Additionally, it maintains the computational efficiency of conventional autoencoders while reducing the number of fully connected layers, resulting in a more sustainable and efficient solution. The method demonstrates how leveraging physics-inspired consistency principles enhances anomaly detection and strengthens the resilience of cyber-physical systems.

Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems

The paper "Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems" by Michael Somma introduces a novel approach to anomaly detection within cyber-physical systems, particularly focusing on water distribution networks. The research addresses the challenges posed by the rapid digitalization of critical infrastructure, where increased integration with IoT and industrial control systems (ICS) exposes new vulnerabilities. The primary goal of the paper is to enhance the effectiveness and efficiency of intrusion detection systems (IDS) in such environments.

Methodology

The paper builds upon the Temporal Differential Consistency (TDC) loss framework, a concept designed to capture dynamic states in the system by ensuring meaningful relationships between these states. The authors propose the Hybrid Temporal Differential Consistency Autoencoder (hTDC-AE), which integrates deterministic and statistical nodes in its structure. This design allows the model to recognize both deterministic processes, which can be represented by physical laws, and non-deterministic processes that statistical methods typically capture.

The methodology focuses on the BATADAL dataset, specific to water distribution systems, enabling the evaluation of the proposed approach's effectiveness in a real-world context. The dataset division corresponds to physical network topology, aiming to reflect operational relevance. Performance metrics, including confusion matrices, F1-score, and time-to-detect anomalies, provide a robust framework for evaluation.

Results and Analysis

The research demonstrates that the hTDC-AE achieves state-of-the-art classification performance, reflecting its robust capability in anomaly detection. Importantly, the approach enhances the rapidity of anomaly detection by 3%, outperforming previous benchmarks and achieving superior performance without the need for domain-specific expertise. This is particularly significant in resource-constrained edge applications where computational efficiency and sustainability are paramount.

The paper's methodological contributions include leveraging physics-inspired consistency principles, reducing the number of fully connected layers, and hence computational requirements, compared to conventional methods. The integration of edge computing concepts further underscores the alignment with sustainable practices, emphasizing energy efficiency in the model's design.

Implications

The implications of this research are twofold:

1. Practical Implementations: The ability to achieve high anomaly detection performance without requiring extensive domain knowledge implies broader applicability across various critical infrastructures. This aspect is crucial for operators of water distribution networks and similar cyber-physical systems striving for greater resilience against cyber threats.
2. Theoretical Development: By introducing a hybrid architecture that effectively balances deterministic and non-deterministic model components, the research offers a direction for further exploration into more interpretable AI models. This could enhance the understanding of the underlying causal mechanisms in complex systems.

Future Directions

The paper opens avenues for further research into improving machine learning interpretability by integrating deterministic principles into autoencoders. There's potential to investigate such hybrid models within simpler systems to paper the theoretical underpinnings and possibly unearth generalizable laws governing cyber-physical systems. This approach might lead to models capable of generalizing across diverse scenarios, thereby broadening the scope of anomaly detection applications.

In conclusion, this research provides a significant contribution to the field of anomaly detection within cyber-physical systems. It balances high performance with computational efficiency, addressing critical real-world requirements and hinting at a shift towards more physically-grounded machine learning models in this domain.