A Bilevel Optimization Framework for Adversarial Control of Gas Pipeline Operations

• URL: http://arxiv.org/abs/2510.02503v1
• Date: Thu, 02 Oct 2025 19:13:41 GMT
• Title: A Bilevel Optimization Framework for Adversarial Control of Gas Pipeline Operations
• Authors: Tejaswini Sanjay Katale, Lu Gao, Yunpeng Zhang, Alaa Senouci,
• Abstract summary: Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure.<n>This study develops a physics-informed simulation and optimization framework for analyzing cyber-physical threats.<n>Test gas pipeline case studies demonstrate the covert reduction of service delivery under attack.
• Score: 11.623882102791399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber-physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bi-level formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bi-level optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack-control interaction is solved via Karush-Kuhn-Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction of service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber-physical infrastructure.

Related papers

• Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control [61.155940786140455]
  Reinforcement learning (RL) has shown promising results in active flow control (AFC)<n>Current AFC benchmarks rely on external computational fluid dynamics (CFD) solvers, are not fully differentiable, and provide limited 3D and multi-agent support.<n>We introduce FluidGym, the first standalone, fully differentiable benchmark suite for RL in AFC.
  arXiv  Detail & Related papers  (2026-01-21T14:13:44Z)
• AquaSentinel: Next-Generation AI System Integrating Sensor Networks for Urban Underground Water Pipeline Anomaly Detection via Collaborative MoE-LLM Agent Architecture [11.644739814142502]
  AquaSentinel is a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks.<n>We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure.
  arXiv  Detail & Related papers  (2025-11-19T20:53:50Z)
• Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols [80.68060125494645]
  We study adaptive attacks by an untrusted model that knows the protocol and the monitor model.<n>We instantiate a simple adaptive attack vector by which the attacker embeds publicly known or zero-shot prompt injections in the model outputs.
  arXiv  Detail & Related papers  (2025-10-10T15:12:44Z)
• Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows [109.99020160824553]
  We propose an efficient deep reinforcement learning framework for modeling and control of turbulent flows.<n>It is model-based RL for predictive control (PC), where both the policy and the observer models for turbulence control are learned jointly.<n>We find that PINO-PC achieves a drag reduction of 39.0% under a bulk-velocity Reynolds number of 15,000, outperforming previous fluid control methods by more than 32%.
  arXiv  Detail & Related papers  (2025-10-03T00:18:26Z)
• Explainable and Resilient ML-Based Physical-Layer Attack Detectors [46.30085297768888]
  We analyze the inner workings of various classifiers trained to alert about physical layer intrusions.<n>We evaluate the detectors' resilience to malicious parameter noising.<n>This work serves as a design guideline for developing fast and robust detectors trained on available network monitoring data.
  arXiv  Detail & Related papers  (2025-09-30T17:05:33Z)
• ViSTR-GP: Online Cyberattack Detection via Vision-to-State Tensor Regression and Gaussian Processes in Automated Robotic Operations [5.95097350945477]
  Connected and automated factories face growing cybersecurity risks that can potentially cause interruptions and damages to physical operations.<n>Data-integrity attacks often involve sophisticated exploitation of vulnerabilities that enable an attacker to access and manipulate the operational data.<n>This paper develops an online detection framework, ViSTR-GP, that cross-checks encoder-reported measurements against a vision-based estimate from an overhead camera outside the controller's authority.
  arXiv  Detail & Related papers  (2025-09-13T19:10:35Z)
• CANDoSA: A Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN bus [45.24207460381396]
  This paper presents a novel Intrusion Detection System (IDS) designed for the Controller Area Network (CAN) environment.<n>A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks.<n>Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.
  arXiv  Detail & Related papers  (2025-07-19T20:09:52Z)
• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• Breaking the Flow and the Bank: Stealthy Cyberattacks on Water Network Hydraulics [3.360922672565235]
  Stealthy False Data Injection Attacks (SFDIAs) can compromise system operations while avoiding detection.<n>This paper presents a systematic analysis of sensor attacks against water distribution networks (WDNs)<n>We propose several attack formulations that range from tailored strategies satisfying both physical and detection constraints to simpler measurement manipulations.
  arXiv  Detail & Related papers  (2025-04-24T02:54:20Z)
• MDHP-Net: Detecting an Emerging Time-exciting Threat in IVN [42.74889568823579]
  We identify a new time-exciting threat model against in-vehicle network (IVN)<n>These attacks inject malicious messages that exhibit a time-exciting effect, gradually manipulating network traffic to disrupt vehicle operations and compromise safety-critical functions.<n>To detect time-exciting threat, we introduce MDHP-Net, leveraging Multi-Dimentional Hawkes Process (MDHP) and temporal and message-wise feature extracting structures.
  arXiv  Detail & Related papers  (2024-11-15T15:05:01Z)
• Model Predictive Control with adaptive resilience for Denial-of-Service Attacks mitigation on a Regulated Dam [5.32980262772932]
  SCADA (Supervisory Control and Data Acquisition) systems have increasingly become the target of cyber attacks. In a cyber-warfare context, we propose a Model Predictive Control architecture with adaptive resilience. We demonstrate the resulting MPC strategy's effectiveness in 2 attack scenarios on a real system with actual data.
  arXiv  Detail & Related papers  (2024-02-28T17:47:27Z)
• Exploring Attack Resilience in Distributed Platoon Controllers with Model Predictive Control [0.0]
  This thesis aims to improve the security of distributed vehicle platoon controllers by investigating attack scenarios and assessing their influence on system performance. Attack techniques, including man-in-the-middle (MITM) and false data injection (FDI), are simulated using Model Predictive Control (MPC) controller. Countermeasures are offered and tested, that includes attack analysis and reinforced communication protocols using Machine Learning techniques for detection.
  arXiv  Detail & Related papers  (2024-01-08T20:27:16Z)
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems [44.99833362998488]
  Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. We combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$$O and a MAPE of 7%.
  arXiv  Detail & Related papers  (2023-11-17T15:30:12Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Active Fuzzing for Testing and Securing Cyber-Physical Systems [8.228859318969082]
  We propose active fuzzing, an automatic approach for finding test suites of packet-level CPS network attacks. Key to our solution is the use of online active learning, which iteratively updates the models by sampling payloads. We evaluate the efficacy of active fuzzing by implementing it for a water purification plant testbed, finding it can automatically discover a test suite of flow, pressure, and over/underflow attacks.
  arXiv  Detail & Related papers  (2020-05-28T16:19:50Z)

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.