Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systems

• URL: http://arxiv.org/abs/2507.04100v1
• Date: Sat, 05 Jul 2025 16:59:57 GMT
• Title: Hierarchical Testing with Rabbit Optimization for Industrial Cyber-Physical Systems
• Authors: Jinwei Hu, Zezhi Tang, Xin Jin, Benyuan Zhang, Yi Dong, Xiaowei Huang,
• Abstract summary: HERO is a black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems.<n> HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective.
• Score: 13.022214684940412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents HERO (Hierarchical Testing with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems. Leveraging Artificial Rabbit Optimization, HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective. Its generalizability ensures applicability across diverse ICPS scenarios. This study specifically focuses on the Proton Exchange Membrane Fuel Cell system, chosen for its highly dynamic operational conditions, complex degradation mechanisms, and increasing integration into ICPS as a sustainable and efficient energy solution. Experimental results highlight HERO's ability to uncover vulnerabilities in even state-of-the-art PHM models, underscoring the critical need for enhanced robustness in real-world applications. By addressing these challenges, HERO demonstrates its potential to advance more resilient PHM systems across a wide range of ICPS domains.

Related papers

• White-Basilisk: A Hybrid Model for Code Vulnerability Detection [50.49233187721795]
  We introduce White-Basilisk, a novel approach to vulnerability detection that demonstrates superior performance.<n>White-Basilisk achieves results in vulnerability detection tasks with a parameter count of only 200M.<n>This research establishes new benchmarks in code security and provides empirical evidence that compact, efficiently designed models can outperform larger counterparts in specialized tasks.
  arXiv  Detail & Related papers  (2025-07-11T12:39:25Z)
• Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis [5.881825061973424]
  Graph Neural Networks (GNNs) show great promise for Network Intrusion Detection Systems (NIDS)<n>GNNs suffer performance degradation due to distribution drift and lack robustness against realistic adversarial attacks.<n>This work proposes a novel approach to enhance GNN robustness and generalization by employing Large Language Models (LLMs) in an agentic pipeline as simulated cybersecurity expert agents.
  arXiv  Detail & Related papers  (2025-06-25T19:49:55Z)
• EyeSim-VQA: A Free-Energy-Guided Eye Simulation Framework for Video Quality Assessment [68.77813885751308]
  EyeSimVQA is a novel VQA framework that incorporates free-energy-based self-repair.<n>We show EyeSimVQA achieves competitive or superior performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-06-13T08:00:54Z)
• Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection [38.083049237330826]
  This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weaknessions (CWEs)<n>Our results indicate that while zero-shot prompting performs poorly, few-shot prompting significantly enhances classification performance.<n> challenges such as model reliability, interpretability, and adversarial robustness remain critical areas for future research.
  arXiv  Detail & Related papers  (2025-06-11T18:43:51Z)
• Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach [50.52139512096988]
  6G Internet of Things (IoT) networks face challenges in remote areas and disaster scenarios where ground infrastructure is unavailable.<n>This paper proposes a novel aerial unmanned vehicle (UAV)-assisted computing (MEC) system enhanced by directional antennas to provide both computational and energy support for ground edge terminals.
  arXiv  Detail & Related papers  (2025-05-06T06:46:19Z)
• Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems [0.0]
  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.
  arXiv  Detail & Related papers  (2025-04-08T09:22:44Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems [1.1034992901877594]
  Large-scale foundation models (LFMs) like BERT and GPT signifies a significant advancement in AI technology. ChatGPT stands as a remarkable accomplishment within this research paradigm, harboring potential for General Artificial Intelligence. Considering the ongoing enhancement in data acquisition technology and data processing capability, LFMs are anticipated to assume a crucial role in the PHM domain of ICPS.
  arXiv  Detail & Related papers  (2023-12-11T09:58:46Z)
• Enhancing Energy Efficiency and Reliability in Autonomous Systems Estimation using Neuromorphic Approach [0.0]
  This study focuses on introducing an estimation framework based on spike coding theories and spiking neural networks (SNN) We propose an SNN-based Kalman filter (KF), a fundamental and widely adopted optimal strategy for well-defined linear systems. Based on the modified sliding innovation filter (MSIF) we present a robust strategy called SNN-MSIF.
  arXiv  Detail & Related papers  (2023-07-16T06:47:54Z)
• Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study [71.84852429039881]
  Investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise. We test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples.
  arXiv  Detail & Related papers  (2023-06-13T12:43:59Z)
• Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders [15.839321488352535]
  We propose a masked spatial-spectral autoencoder (MSSA) for enhancing the robustness of HSI analysis systems. To improve the defense transferability and address the problem of limited labelled samples, MSSA employs spectra reconstruction as a pretext task. Comprehensive experiments over three benchmarks verify the effectiveness of MSSA in comparison with the state-of-the-art hyperspectral classification methods.
  arXiv  Detail & Related papers  (2022-07-16T01:33:13Z)
• Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles [55.23285485923913]
  Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives. In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions. This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems.
  arXiv  Detail & Related papers  (2021-11-04T20:18:55Z)

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.