# Anomaly Detection from Cyber Threats via Infrastructure to Automated Vehicle

**Authors:** Chris van der Ploeg, Robin Smit, Alexis Siagkris-Lekkos, Frank Benders, Emilia Silvas  
**arXiv:** [2104.11790](https://arxiv.org/abs/2104.11790)  
**Date:** 2021-04-23  
**Summary page:** https://www.summarizepaper.com/en/paper/2104.11790v1/

## Key points

- The research paper explores the use of Infrastructure-to-Vehicle (I2V) information in autonomous driving scenarios with limited visibility and high-density traffic.
- External sensors are highlighted as beneficial for enhancing the vehicle's sensing capabilities.
- Cyber security is a major challenge associated with I2V communication.
- The researchers propose an anomaly detection algorithm to increase the vehicle's self-awareness in intersections.
- The algorithm assesses the health of I2V communication against various cyber security attacks.
- The analysis is conducted in a simulation environment using cyber-attack scenarios from the Secredas Project.
- The study demonstrates that anomalies injected during these attacks can be robustly detected and mitigated by the autonomous vehicle.
- The paper has been accepted and submitted for presentation at IEEE European Control Conference 2021.

## Summary

The research paper titled "Anomaly Detection from Cyber Threats via Infrastructure to Automated Vehicle" explores the use of Infrastructure-to-Vehicle (I2V) information in autonomous driving scenarios with limited visibility and high-density traffic. The authors highlight the benefits of utilizing external sensors to enhance the vehicle's sensing capabilities. However, one of the major challenges associated with I2V communication is cyber security. To address this challenge, the researchers propose a method to increase the vehicle's self-awareness in intersections by introducing an anomaly detection algorithm. This algorithm, implemented on the automated vehicle, assesses the health of I2V communication against various cyber security attacks. The analysis is conducted in a simulation environment using cyber-attack scenarios from the Secredas Project, which focuses on Cyber Security for Cross Domain Reliable Dependable Automated Systems. The study provides valuable insights into the limitations faced by autonomous vehicles when encountering different types and amplitudes of I2V cyber attacks, particularly when sensor redundancy is lost. The results demonstrate that anomalies injected during these attacks can be robustly detected and mitigated by the autonomous vehicle. This capability allows for safer and more comfortable driving experiences while maintaining accurate object tracking in intersections. The paper is authored by Chris van der Ploeg, Robin Smit, Alexis Siagkris-Lekkos, Frank Benders, and Emilia Silvas and has been accepted and submitted for presentation at IEEE European Control Conference 2021. It consists of 7 pages and includes 8 figures illustrating key concepts.