Spatiotemporal Information Based Intrusion Detection Systems for In-Vehicle Networks

Xiangxue Li; Yue Bao; Xintian Hou

doi:10.1007/978-3-031-28016-0_14

Spatiotemporal Information Based Intrusion Detection Systems for In-Vehicle Networks

Xiangxue Li^*, Yue Bao, Xintian Hou

^*Corresponding author for this work

Software Engineering Institute

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Since communication security is not a primary concern at the beginning of in-vehicle network protocol design (e.g., controller area network, CAN), it is not a surprise that in-vehicle networks are exposed to numerous security threats. As vehicles are safety-critical, practical and effective steps should be taken to protect drivers and passengers. This chapter describes intrusion detection systems (IDS) on in-vehicle networks for reinforcing CAN security. These IDS mechanisms rely on spatiotemporal information of CAN data frames. Given limited computational power of in-vehicle electronic control units, lightweight IDS is preferred.

Original language	English
Title of host publication	Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems
Publisher	Springer International Publishing
Pages	425-451
Number of pages	27
ISBN (Electronic)	9783031280160
ISBN (Print)	9783031280153
DOIs	https://doi.org/10.1007/978-3-031-28016-0_14
State	Published - 1 Jan 2023

Keywords

CAN frames
In-vehicle networks
Intrusion detection systems
Machine learning
Robustness
Spatiotemporal information

Access to Document

10.1007/978-3-031-28016-0_14

Cite this

@inbook{d2df12bdcda344f6b9361bed0b347fe3,

title = "Spatiotemporal Information Based Intrusion Detection Systems for In-Vehicle Networks",

abstract = "Since communication security is not a primary concern at the beginning of in-vehicle network protocol design (e.g., controller area network, CAN), it is not a surprise that in-vehicle networks are exposed to numerous security threats. As vehicles are safety-critical, practical and effective steps should be taken to protect drivers and passengers. This chapter describes intrusion detection systems (IDS) on in-vehicle networks for reinforcing CAN security. These IDS mechanisms rely on spatiotemporal information of CAN data frames. Given limited computational power of in-vehicle electronic control units, lightweight IDS is preferred.",

keywords = "CAN frames, In-vehicle networks, Intrusion detection systems, Machine learning, Robustness, Spatiotemporal information",

author = "Xiangxue Li and Yue Bao and Xintian Hou",

note = "Publisher Copyright: {\textcopyright} The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.",

year = "2023",

month = jan,

day = "1",

doi = "10.1007/978-3-031-28016-0\_14",

language = "英语",

isbn = "9783031280153",

pages = "425--451",

booktitle = "Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems",

publisher = "Springer International Publishing",

address = "瑞士",

}

TY - CHAP

T1 - Spatiotemporal Information Based Intrusion Detection Systems for In-Vehicle Networks

AU - Li, Xiangxue

AU - Bao, Yue

AU - Hou, Xintian

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Since communication security is not a primary concern at the beginning of in-vehicle network protocol design (e.g., controller area network, CAN), it is not a surprise that in-vehicle networks are exposed to numerous security threats. As vehicles are safety-critical, practical and effective steps should be taken to protect drivers and passengers. This chapter describes intrusion detection systems (IDS) on in-vehicle networks for reinforcing CAN security. These IDS mechanisms rely on spatiotemporal information of CAN data frames. Given limited computational power of in-vehicle electronic control units, lightweight IDS is preferred.

AB - Since communication security is not a primary concern at the beginning of in-vehicle network protocol design (e.g., controller area network, CAN), it is not a surprise that in-vehicle networks are exposed to numerous security threats. As vehicles are safety-critical, practical and effective steps should be taken to protect drivers and passengers. This chapter describes intrusion detection systems (IDS) on in-vehicle networks for reinforcing CAN security. These IDS mechanisms rely on spatiotemporal information of CAN data frames. Given limited computational power of in-vehicle electronic control units, lightweight IDS is preferred.

KW - CAN frames

KW - In-vehicle networks

KW - Intrusion detection systems

KW - Machine learning

KW - Robustness

KW - Spatiotemporal information

UR - https://www.scopus.com/pages/publications/85195918030

U2 - 10.1007/978-3-031-28016-0_14

DO - 10.1007/978-3-031-28016-0_14

M3 - 章节

AN - SCOPUS:85195918030

SN - 9783031280153

SP - 425

EP - 451

BT - Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems

PB - Springer International Publishing

ER -

Spatiotemporal Information Based Intrusion Detection Systems for In-Vehicle Networks

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this