# Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles

arXiv: https://arxiv.org/abs/2108.00833 · HTML: https://mlsanity.com/paper/2108.00833/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks](https://mlsanity.com/paper/2105.15022/) (2021)
- [DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles](https://mlsanity.com/paper/2106.06291/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach](https://mlsanity.com/paper/2311.18498/) (2023)
- [GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV](https://mlsanity.com/paper/2204.11010/) (2022)

## Abstract (arXiv)

Machine learning (ML) has made incredible impacts and transformations in a wide range of vehicular applications. As the use of ML in Internet of Vehicles (IoV) continues to advance, adversarial threats and their impact have become an important subject of research worth exploring. In this paper, we focus on Sybil-based adversarial threats against a deep reinforcement learning (DRL)-assisted IoV framework and more specifically, DRL-based dynamic service placement in IoV. We carry out an experimental study with real vehicle trajectories to analyze the impact on service delay and resource congestion under different attack scenarios for the DRL-based dynamic service placement application. We further investigate the impact of the proportion of Sybil-attacked vehicles in the network. The results demonstrate that the performance is significantly affected by Sybil-based data poisoning attacks when compared to adversary-free healthy network scenario.