# Detecting subtle cyberattacks on adaptive cruise control vehicles: A machine learning approach

arXiv: https://arxiv.org/abs/2310.17091 · HTML: https://mlsanity.com/paper/2310.17091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 57 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems](https://mlsanity.com/paper/2003.07859/) (2020)
- [TAnoGAN: Time Series Anomaly Detection with Generative Adversarial Networks](https://mlsanity.com/paper/2008.09567/) (2020)
- [Are commercially implemented adaptive cruise control systems string stable?](https://mlsanity.com/paper/1905.02108/) (2019)
- [Calibrating microscopic car following models for adaptive cruise control vehicles: a multi-objective approach](https://mlsanity.com/paper/2010.03109/) (2020)
- [A Secure Sensor Fusion Framework for Connected and Automated Vehicles under Sensor Attacks](https://mlsanity.com/paper/2103.00883/) (2021)
- [Compromised ACC vehicles can degrade current mixed-autonomy traffic performance while remaining stealthy against detection](https://mlsanity.com/paper/2112.11986/) (2021)

## Built on by (most cited first, 1 of 1)

- [Learn2Drive: A neural network-based framework for socially compliant automated vehicle control](https://mlsanity.com/paper/2510.21736/) (2025)

## Abstract (arXiv)

With the advent of vehicles equipped with advanced driver-assistance systems, such as adaptive cruise control (ACC) and other automated driving features, the potential for cyberattacks on these automated vehicles (AVs) has emerged. While overt attacks that force vehicles to collide may be easily identified, more insidious attacks, which only slightly alter driving behavior, can result in network-wide increases in congestion, fuel consumption, and even crash risk without being easily detected. To address the detection of such attacks, we first present a traffic model framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, false data injection attacks on sensor measurements, and denial-of-service (DoS) attacks. We then investigate the impacts of these attacks at both the individual vehicle (micro) and traffic flow (macro) levels. A novel generative adversarial network (GAN)-based anomaly detection model is proposed for real-time identification of such attacks using vehicle trajectory data. We provide numerical evidence {to demonstrate} the efficacy of our machine learning approach in detecting cyberattacks on ACC-equipped vehicles. The proposed method is compared against some recently proposed neural network models and observed to have higher accuracy in identifying anomalous driving behaviors of ACC vehicles.