A Bilevel Optimization Framework for Adversarial Control of Gas Pipeline Operations

2025-10-02

AI Summary

This study addresses stealthy false data injection (FDI) attacks in oil and gas pipeline networks—attacks that evade detection by manipulating sensor measurements to disrupt operational control and reduce throughput, thereby compromising energy infrastructure security. We propose a physics-informed bilevel optimization model that formalizes the strategic interaction between attacker and controller; leveraging Karush–Kuhn–Tucker (KKT) conditions, we reformulate it as a tractable mixed-integer quadratic program. To enable high-fidelity evaluation, we integrate graph-based hydraulic dynamics, extended Kalman filtering for state estimation, model predictive control, and SCADA telemetry into a comprehensive simulation environment. Case studies demonstrate that the proposed attack persistently degrades system delivery capacity without triggering bad data detection, exposing structural vulnerabilities of current cyber-physical control architectures under stealthy adversarial conditions. The work establishes a novel paradigm for co-designed cyber-physical defense, advancing intelligent sensing and robust control strategies.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Large-scale security measurements

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.

Problem

• Modeling stealthy cyberattacks on gas pipeline operational technology systems
• Developing bi-level optimization for adversarial false-data injection attacks
• Analyzing undetectable throughput reduction in cyber-physical pipeline networks

Innovation

• Bilevel optimization for stealthy false-data injection attacks
• Physics-informed simulation integrating hydraulic dynamics and MPC
• KKT reformulation enabling tractable mixed-integer quadratic programming