A Resilient Control Strategy for Train-to-Train Communications under Jamming Attacks

Communication-Based Train Control (CBTC) systems rely on wireless communications to enhance the efficiency of railway operations. Classical CBTC systems incorporate bidirectional train-to-wayside (T2W) communications through which trains send their status information to wayside units. T2T-CBTC systems represent a burgeoning direction in the future of CBTC. They adopt train-to-train (T2T) communications for adjacent trains to share status information. T2T communications simplify the architecture of traditional CBTC networks and reduce transmission delays. Wireless communications can introduce cybersecurity threats to inter-train communications. This work proposes two resilient control strategies for T2T-CBTC systems to mitigate the effects of jamming. Both strategies are based on multi-agent deep reinforcement learning and aim to control trains’ operations under jamming attacks, allowing them to continue operating safely instead of applying emergency braking. One strategy is based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, and the other is based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm. The strategies are implemented and compared with an existing strategy based on MADDPG. The experimental results indicate that the proposed MADDPG-based strategy shortens the convergence time by 32% to 42%, while the MATD3-based strategy achieves a reduction of 40% to 48%, compared to the baseline.

• Publication year

  2025

• Venue

  ACM Transactions on Autonomous and Adaptive Systems

• Publication date

  2025-09-01

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1145/3759919
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Graph neural network‐based attack prediction for communication‐based train control systems

  2024cited by this paper
• Privacy-Aware Offloading Strategy via Self-Supervised Feature Mapping in the End-Edge-Cloud System

  2024cited by this paper
• TrainSec: A Simulation Framework for Security Modeling and Evaluation in CBTC Networks

  2023influential reference
• A User Study on Explainable Online Reinforcement Learning for Adaptive Systems

  2023cited by this paper
• Joint Security and Resilience Control in IIoT-Based Virtual Control Train Sets Under Jamming Attacks

  2023cited by this paper
• CBTCset: A Reference Dataset for Detecting Misbehavior Attacks in CBTC Networks

  2023cited by this paper
• Secure Data Sharing With Flexible Cross-Domain Authorization in Autonomous Vehicle Systems

  2023cited by this paper
• A Comprehensive Resilient Control Strategy for CBTC Systems Through Train-to-Train Communications Under Malicious Attacks

  2022cited by this paper
• Attack-Resilient Optimal PMU Placement via Reinforcement Learning Guided Tree Search in Smart Grids

  2022cited by this paper
• Blockchain-Integrated Multiagent Deep Reinforcement Learning for Securing Cooperative Adaptive Cruise Control

  2022cited by this paper
• Practical Privacy-Preserving Federated Learning in Vehicular Fog Computing

  2022cited by this paper
• Cross-Layer Defense Methods for Jamming-Resistant CBTC Systems

  2021cited by this paper
• A Reinforcement Learning-Based Vehicle Platoon Control Strategy for Reducing Energy Consumption in Traffic Oscillations

  2021cited by this paper
• Deep Reinforcement Learning-based Resilient Control Method for CBTC Systems through Train-to-Train Communications under Adversarial Attacks

  2021influential reference
• Multi-agent deep reinforcement learning: a survey

  2021cited by this paper
• Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

  2020cited by this paper
• Enhancing Transmission on Hybrid Precoding Based Train-to-Train Communication

  2020cited by this paper
• Train-Centric CBTC Meets Age of Information in Train-to-Train Communications

  2020cited by this paper
• Intelligent Resource Allocation for Train-to-Train Communication: A Multi-Agent Deep Reinforcement Learning Approach

  2020influential reference
• Security and Privacy Challenges in 5G-Enabled Vehicular Networks

  2020cited by this paper
• Deep reinforcement learning approach for autonomous vehicle systems for maintaining security and safety using LSTM-GAN

  2020cited by this paper
• PettingZoo: Gym for Multi-Agent Reinforcement Learning

  2020cited by this paper
• UAV-Enabled Secure Communications by Multi-Agent Deep Reinforcement Learning

  2020cited by this paper
• A Virtual Coupling Approach Based on Event-triggering Control for CBTC Systems under Jamming Attacks

  2020cited by this paper
• Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms

  2019cited by this paper
• Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics

  2019influential reference
• A review of cooperative multi-agent deep reinforcement learning

  2019cited by this paper
• Direct train-to-train communications at low UHF frequencies

  2018cited by this paper
• Energy-efficiency opportunistic spectrum allocation in cognitive wireless sensor network

  2018cited by this paper
• A survey and critique of multiagent deep reinforcement learning

  2018cited by this paper
• Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

  2017influential reference
• Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition

  2017cited by this paper
• Communication-Based Train Control System Performance Optimization Using Deep Reinforcement Learning

  2017cited by this paper
• A Game-Theoretic Approach to Secure Control of Communication-Based Train Control Systems Under Jamming Attacks

  2017cited by this paper
• Asynchronous Methods for Deep Reinforcement Learning

  2016cited by this paper
• Spatial Spectrum Sensing-Based Device-to-Device Cellular Networks

  2016cited by this paper
• A Cognitive Control Approach to Communication-Based Train Control Systems

  2015cited by this paper
• Cross-Layer Handoff Design in MIMO-Enabled WLANs for Communication-Based Train Control (CBTC) Systems

  2012cited by this paper
• What is communication-based train control?

  2009cited by this paper
• A Comprehensive Survey of Multiagent Reinforcement Learning

  2008cited by this paper

• No citing papers are available for this paper.