Secure Connected and Automated Vehicles against False Data Injection Attack using Cloud-based Data Fusion

被引：2

作者：

Zhao, Chunheng ^{[1
]}

Comert, Gurcan ^{[2
]}

Pisu, Pierluigi ^{[1
]}

机构：

[1] Clemson Univ, Int Ctr Automot Res, Greenville, SC 29607 USA

[2] Benedict Coll, Columbia, SC 29204 USA

来源：

IFAC PAPERSONLINE | 2021年 / 54卷 / 20期

基金：

美国国家科学基金会;

关键词：

Connected and automated vehicles; Data fusion; Particle filter; Attack detection;

D O I：

10.1016/j.ifacol.2021.11.243

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

It has been shown that interdependency in connected and automated vehicles (CAV) can be potentially beneficial in several aspects, however, it also poses a set of specific challenges in concern of safety and reliability due to the possibility of cyber-attacks. In this paper, we present a data fusion-based methodology to detect the false data injection (FDI) attack on CAVs, and generate a flow of trustworthy information for every CAV. The effectiveness of the proposed approach is validated using microscopic traffic simulation, which shows that our proposed methodology is able to detect and isolate the false data injection attacks on CAVs. Copyright (C) 2021 The Authors.

引用

页码：638 / 643

页数：6

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