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

• URL: http://arxiv.org/abs/2504.06320v2
• Date: Fri, 19 Sep 2025 06:15:56 GMT
• Title: Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems
• Authors: Michael Somma,
• Abstract summary: Cyberattacks on critical infrastructure, particularly water distribution systems, have increased due to rapid digitalization.<n>This study addresses key challenges in anomaly detection by leveraging time correlations in sensor data.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• A Quantum Genetic Algorithm-Enhanced Self-Supervised Intrusion Detection System for Wireless Sensor Networks in the Internet of Things [1.049126606580198]
  This paper proposes a novel hybrid Intrusion Detection System that integrates a Quantum Genetic Algorithm (QGA) with Self-Supervised Learning (SSL)<n>The proposed framework is evaluated on benchmark IoT intrusion datasets, demonstrating superior performance in terms of detection accuracy, false positive rate, and computational efficiency.
  arXiv  Detail & Related papers  (2025-09-03T22:02:39Z)
• Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT [3.6686692131754834]
  The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges.<n>Traditional Intrusion Detection Systems (IDS) often overlook the temporal characteristics of network traffic.<n>We propose a Transformer-based Early Intrusion Detection System (EIDS) that incorporates dynamic temporal positional encodings.
  arXiv  Detail & Related papers  (2025-06-22T17:56:19Z)
• A Hybrid Artificial Intelligence Method for Estimating Flicker in Power Systems [44.99833362998488]
  This paper introduces a novel hybrid AI method combining H filtering and an adaptive linear neuron network for flicker component estimation in power distribution systems.<n>The proposed method leverages the robustness of the H filter to extract the voltage envelope under uncertain and noisy conditions followed by the use of ADALINE to accurately identify flicker frequencies embedded in the envelope.
  arXiv  Detail & Related papers  (2025-06-16T15:38:39Z)
• Distributed Log-driven Anomaly Detection System based on Evolving Decision Making [4.183506125389502]
  CEDLog is a framework that implements distributed computing for scalable processing by integrating Apache Airflow and Dask.<n>In CEDLog, anomalies are detected through the synthesis of Multi-layer Perceptron (MLP) and Graph Convolutional Networks (GCNs) using critical features present in event logs.
  arXiv  Detail & Related papers  (2025-04-03T06:50:30Z)
• Enhancing Network Security Management in Water Systems using FM-based Attack Attribution [43.48086726793515]
  We propose a novel model-agnostic Factorization Machines (FM)-based approach that capitalizes on water system sensor-actuator interactions to provide granular explanations and attributions for cyber attacks.<n>In multi-feature cyber attack scenarios involving intricate sensor-actuator interactions, our FM-based attack attribution method effectively ranks attack root causes, achieving approximately 20% average improvement over SHAP and LEMNA.
  arXiv  Detail & Related papers  (2025-03-03T06:52:00Z)
• Anomaly Detection in Complex Dynamical Systems: A Systematic Framework Using Embedding Theory and Physics-Inspired Consistency [0.0]
  Anomaly detection in complex dynamical systems is essential for ensuring reliability, safety, and efficiency in industrial and cyber-physical infrastructures.<n>We propose a system-theoretic approach to anomaly detection, grounded in classical embedding theory and physics-inspired consistency principles.<n>Our findings support the hypothesis that anomalies disrupt stable system dynamics, providing a robust, interpretable signal for anomaly detection.
  arXiv  Detail & Related papers  (2025-02-26T17:06:13Z)
• Leveraging Conversational Generative AI for Anomaly Detection in Digital Substations [0.0]
  The research employs advanced performance metrics to conduct a comparative assessment between the proposed AD and HITL-based AD frameworks.<n>This approach presents a promising solution for enhancing the reliability of power system operations in the face of evolving cybersecurity challenges.
  arXiv  Detail & Related papers  (2024-11-09T18:38:35Z)
• Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
  In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions. This paper proposes a digital twin (DT) and federated digital twin (FL) scheme. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
  arXiv  Detail & Related papers  (2024-11-04T17:48:02Z)
• Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors [41.94295877935867]
  This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft. A multi-channel Convolutional Neural Network (CNN) is used to perform multi-target classification and independently detect faults in the sensors. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level.
  arXiv  Detail & Related papers  (2024-10-11T09:36:38Z)
• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.<n>By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.<n>GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies [41.994460245857404]
  This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources. We develop a physics-based AI foundation model with high-resolution synchro-waveform measurement technology to enhance grid resilience and reduce economic losses from outages.
  arXiv  Detail & Related papers  (2024-03-11T17:28:46Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• A Variational Autoencoder Framework for Robust, Physics-Informed Cyberattack Recognition in Industrial Cyber-Physical Systems [2.051548207330147]
  We develop a data-driven framework that can be used to detect, diagnose, and localize a type of cyberattack called covert attacks on industrial control systems. The framework has a hybrid design that combines a variational autoencoder (VAE), a recurrent neural network (RNN), and a Deep Neural Network (DNN)
  arXiv  Detail & Related papers  (2023-10-10T19:07:53Z)
• TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems [43.39754726042369]
  We propose a simple-yet-effective self-supervised regularization term as a soft constraint that aligns the forward and backward trajectories predicted by a continuous graph neural network-based ordinary differential equation (GraphODE) It effectively imposes time-reversal symmetry to enable more accurate model predictions across a wider range of dynamical systems under classical mechanics. Experimental results on a variety of physical systems demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-10-10T08:52:16Z)
• AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios [51.94807626839365]
  We propose the attention-inspired numerical solver (AttNS) to solve differential equations due to limited data.<n>AttNS is inspired by the effectiveness of attention modules in Residual Neural Networks (ResNet) in enhancing model generalization and robustness.
  arXiv  Detail & Related papers  (2023-02-05T01:39:21Z)
• On a Uniform Causality Model for Industrial Automation [61.303828551910634]
  A Uniform Causality Model for various application areas of industrial automation is proposed. The resulting model describes the behavior of Cyber-Physical Systems mathematically. It is shown that the model can work as a basis for the application of new approaches in industrial automation that focus on machine learning.
  arXiv  Detail & Related papers  (2022-09-20T11:23:51Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Deep Learning based Covert Attack Identification for Industrial Control Systems [5.299113288020827]
  We develop a data-driven framework that can be used to detect, diagnose, and localize a type of cyberattack called covert attacks on smart grids. The framework has a hybrid design that combines an autoencoder, a recurrent neural network (RNN) with a Long-Short-Term-Memory layer, and a Deep Neural Network (DNN)
  arXiv  Detail & Related papers  (2020-09-25T17:48:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.