# Data Poisoning Attacks in Intelligent Transportation Systems: A Survey

arXiv: https://arxiv.org/abs/2407.15855 · HTML: https://mlsanity.com/paper/2407.15855/

## 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 (most cited first, 25 of 25)

- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Black-box Adversarial Attacks with Limited Queries and Information](https://mlsanity.com/paper/1804.08598/) (2018)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses](https://mlsanity.com/paper/2012.10544/) (2020)
- [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://mlsanity.com/paper/1907.06826/) (2019)
- [Backdoor Attacks Against Deep Learning Systems in the Physical World](https://mlsanity.com/paper/2006.14580/) (2020)
- [Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning](https://mlsanity.com/paper/2205.01992/) (2022)
- [Is feature selection secure against training data poisoning?](https://mlsanity.com/paper/1804.07933/) (2018)
- [Influence Function based Data Poisoning Attacks to Top-N Recommender Systems](https://mlsanity.com/paper/2002.08025/) (2020)
- [Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems](https://mlsanity.com/paper/2003.07859/) (2020)
- [Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://mlsanity.com/paper/2104.01789/) (2021)
- [Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models](https://mlsanity.com/paper/2210.02447/) (2022)
- [Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid](https://mlsanity.com/paper/1708.06939/) (2017)
- [Universal 3-Dimensional Perturbations for Black-Box Attacks on Video Recognition Systems](https://mlsanity.com/paper/2107.04284/) (2021)
- [Who is in Control? Practical Physical Layer Attack and Defense for mmWave based Sensing in Autonomous Vehicles](https://mlsanity.com/paper/2011.10947/) (2020)
- [Extracting Trips from Multi-Sourced Data for Mobility Pattern Analysis: An App-Based Data Example](https://mlsanity.com/paper/1912.01835/) (2019)
- [Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models](https://mlsanity.com/paper/2104.09369/) (2021)
- [Impact Evaluation of Falsified Data Attacks on Connected Vehicle Based Traffic Signal Control](https://mlsanity.com/paper/2010.04753/) (2020)
- [Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning](https://mlsanity.com/paper/2104.08553/) (2021)
- [Infrastructure-enabled GPS Spoofing Detection and Correction](https://mlsanity.com/paper/2202.05463/) (2022)
- [Traffic networks are vulnerable to disinformation attacks](https://mlsanity.com/paper/2003.03723/) (2020)
- [Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment](https://mlsanity.com/paper/2003.04185/) (2020)
- [Evaluating Cybersecurity Risks of Cooperative Ramp Merging in Mixed Traffic Environments](https://mlsanity.com/paper/2111.09521/) (2021)
- [DCA: Delayed Charging Attack on the Electric Shared Mobility System](https://mlsanity.com/paper/2302.01972/) (2023)

## Built on by (most cited first, 5 of 5)

- [Detecting and Preventing Data Poisoning Attacks on AI Models](https://mlsanity.com/paper/2503.09302/) (2025)
- [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024)
- [Robustness of LLM-enabled vehicle trajectory prediction under data security threats](https://mlsanity.com/paper/2511.13753/) (2025)
- [Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Driving](https://mlsanity.com/paper/2601.01800/) (2026)
- [Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectors](https://mlsanity.com/paper/2602.09740/) (2026)

## 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.