mlsanityData to 14 Oct 2026

Data Poisoning Attacks in Intelligent Transportation Systems: A Survey

arXiv 2407.15855 ↗First posted 6 Jul 2024cs.CR cs.LGcited by 5

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Abstract (arXiv)

Emerging technologies drive the ongoing transformation of Intelligent Transportation Systems (ITS). This transformation has given rise to cybersecurity concerns, among which data poisoning attack emerges as a new threat as ITS increasingly relies on data. In data poisoning attacks, attackers inject malicious perturbations into datasets, potentially leading to inaccurate results in offline learning and real-time decision-making processes. This paper concentrates on data poisoning attack models against ITS. We identify the main ITS data sources vulnerable to poisoning attacks and application scenarios that enable staging such attacks. A general framework is developed following rigorous study process from cybersecurity but also considering specific ITS application needs. Data poisoning attacks against ITS are reviewed and categorized following the framework. We then discuss the current limitations of these attack models and the future research directions. Our work can serve as a guideline to better understand the threat of data poisoning attacks against ITS applications, while also giving a perspective on the future development of trustworthy ITS.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 25 of its 115 references that are in the map. First posted on arXiv 6 Jul 2024.

Builds on

25 of 115 references.

References · 25

  1. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  2. 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 501
  3. 2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveycited by 460
  4. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 414
  5. 2020Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defensescited by 180
  6. 2019Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Drivingcited by 152
  7. 2020Backdoor Attacks Against Deep Learning Systems in the Physical Worldcited by 134
  8. 2022Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoningcited by 94
  9. 2018Is feature selection secure against training data poisoning?cited by 93
  10. 2020Influence Function based Data Poisoning Attacks to Top-N Recommender Systemscited by 63
  11. 2020Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systemscited by 37
  12. 2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defensescited by 30
13 more
  1. 2022Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Modelscited by 29
  2. 2017Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoidcited by 19
  3. 2021Universal 3-Dimensional Perturbations for Black-Box Attacks on Video Recognition Systemscited by 14
  4. 2020Who is in Control? Practical Physical Layer Attack and Defense for mmWave based Sensing in Autonomous Vehiclescited by 6
  5. 2019Extracting Trips from Multi-Sourced Data for Mobility Pattern Analysis: An App-Based Data Examplecited by 5
  6. 2021Adversarial Diffusion Attacks on Graph-based Traffic Prediction Modelscited by 5
  7. 2020Impact Evaluation of Falsified Data Attacks on Connected Vehicle Based Traffic Signal Controlcited by 3
  8. 2021Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learningcited by 3
  9. 2022Infrastructure-enabled GPS Spoofing Detection and Correctioncited by 3
  10. 2020Traffic networks are vulnerable to disinformation attackscited by 2
  11. 2020Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environmentcited by 2
  12. 2021Evaluating Cybersecurity Risks of Cooperative Ramp Merging in Mixed Traffic Environmentscited by 2
  13. 2023DCA: Delayed Charging Attack on the Electric Shared Mobility Systemcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2025Detecting and Preventing Data Poisoning Attacks on AI Modelscited by 2
  2. 2024Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learningcited by 0
  3. 2025Robustness of LLM-enabled vehicle trajectory prediction under data security threatscited by 0
  4. 2026Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Drivingcited by 0
  5. 2026Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectorscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Poisoning Attacks and Defenses on Artificial Intelligence: A Survey
  2. 2025A Large Language Model-Supported Threat Modeling Framework for Transportation Cyber-Physical Systems
  3. 2026Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles
  4. 2025Are Targeted Data Poisoning Attacks as Effective as We Think?
  5. 2025Exploring Traffic Simulation and Cybersecurity Strategies Using Large Language Models
  6. 2022Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systems
  7. 2024The Effect of Data Poisoning on Counterfactual Explanations
  8. 2022Federated Learning based on Defending Against Data Poisoning Attacks in IoT