A CAD Framework for Simulation of Network Level Attack on Platoons

TL;DR

This paper introduces an automated simulation framework for testing network-level attacks on vehicle platoons, enabling security assessment and development of attack detection methods in connected vehicle systems.

Contribution

It presents a novel automated tool flow for simulating false data injection attacks on vehicle platoons, aiding in security verification and defense strategy development.

Findings

Allows simulation of attack scenarios on customizable platoons

Supports design and testing of lightweight attack detection algorithms

Facilitates early-stage security assessment of connected vehicle systems

Abstract

Recent developments in the smart mobility domain have transformed automobiles into networked transportation agents helping realize new age, large-scale intelligent transportation systems (ITS). The motivation behind such networked transportation is to improve road safety as well as traffic efficiency. In this setup, vehicles can share information about their speed and/or acceleration values among themselves and infrastructures can share traffic signal data with them. This enables the connected vehicles (CVs) to stay informed about their surroundings while moving. However, the inter-vehicle communication channels significantly broaden the attack surface. The inter-vehicle network enables an attacker to remotely launch attacks. An attacker can create collision as well as hamper performance by reducing the traffic efficiency. Thus, security vulnerabilities must be taken into consideration in the early phase of the development cycle of CVs. To the best of our knowledge, there exists no such automated simulation tool using which engineers can verify the performance of CV prototypes in the presence of an attacker. In this work, we present an automated tool flow that facilitates false data injection attack synthesis and simulation on customizable platoon structure and vehicle dynamics. This tool can be used to simulate as well as design and verify control-theoretic light-weight attack detection and mitigation algorithms for CVs.