Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks

Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the dep...

Full description

Saved in:
Bibliographic Details
Published in:2022 IEEE Intelligent Vehicles Symposium (IV) pp. 1731 - 1738
Main Authors: Alkhatib, Natasha, Mushtaq, Maria, Ghauch, Hadi, Danger, Jean-Luc
Format: Conference Proceeding
Language:English
Published: IEEE 05.06.2022
Subjects:
Anomaly Detection
Automotive Ethernet
AVTP
Deep learning
Ethernet
In-Vehicle Network
Network intrusion detection
Neural Network
Numerical models
Real-time systems
Telecommunication traffic
Transport protocols
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system which play an important role in detecting unknown attack patterns in network traffic. Hence, in this paper, we compare the performance of different unsupervised deep and machine learning based anomaly detection algorithms, for real-time detection of anomalies on the Audio Video Transport Protocol (AVTP), an application layer protocol implemented in the recent Automotive Ethernet based in-vehicle network. The numerical results, conducted on the recently published "Automotive Ethernet Intrusion Dataset show that deep learning models significantly outperfom other state-of-the art traditional anomaly detection models in machine learning under different experimental settings.
AbstractList Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system which play an important role in detecting unknown attack patterns in network traffic. Hence, in this paper, we compare the performance of different unsupervised deep and machine learning based anomaly detection algorithms, for real-time detection of anomalies on the Audio Video Transport Protocol (AVTP), an application layer protocol implemented in the recent Automotive Ethernet based in-vehicle network. The numerical results, conducted on the recently published "Automotive Ethernet Intrusion Dataset show that deep learning models significantly outperfom other state-of-the art traditional anomaly detection models in machine learning under different experimental settings.
Author Ghauch, Hadi
Alkhatib, Natasha
Danger, Jean-Luc
Mushtaq, Maria
Author_xml – sequence: 1
  givenname: Natasha
  surname: Alkhatib
  fullname: Alkhatib, Natasha
  email: natasha.alkhatib@telecom-paris.fr
  organization: Télécom Paris, IP Paris,Palaiseau,France
– sequence: 2
  givenname: Maria
  surname: Mushtaq
  fullname: Mushtaq, Maria
  email: maria.mushtaq@telecom-paris.fr
  organization: Télécom Paris, IP Paris,Palaiseau,France
– sequence: 3
  givenname: Hadi
  surname: Ghauch
  fullname: Ghauch, Hadi
  email: hadi.ghauch@telecom-paris.fr
  organization: Télécom Paris, IP Paris,Palaiseau,France
– sequence: 4
  givenname: Jean-Luc
  surname: Danger
  fullname: Danger, Jean-Luc
  email: jean-luc.danger@telecom-paris.fr
  organization: Télécom Paris, IP Paris,Palaiseau,France
BookMark eNo1j09LwzAcQCPowU0_gR7yBVrzp0nTY5nTFYYKbsPbSLpfWNAmI0kn-_YiztN7pwdvgi598IDQPSUlpaR56DaCNjUtGWGsbBSrmRIXaEKlFJVSjLJr9LH2aTxAPLoEO_wC-TvET9z5HMfkgsePkKHPv_Z-ShkGbEPE7Wb1hp3H7ZjDELI7Ap7nPUQP-T-RbtCV1V8Jbs-covXTfDVbFMvX527WLos943UugEtpxK4XRtmKga0U0VQ3RhnSaCNBEiWpkarXghiQfa2B1wS0Fdb2zFg-RXd_XQcA20N0g46n7XmW_wC_sVEp
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/IV51971.2022.9827285
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 1665488212
9781665488211
EndPage 1738
ExternalDocumentID 9827285
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-h237t-e366b5dc5b8f42ef480a1a9b8b09ab6e60861b68ca50be6c7ae370eaf5ffc2bf3
IEDL.DBID RIE
ISICitedReferencesCount 23
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000854106700245&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Thu Jun 29 18:36:49 EDT 2023
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-h237t-e366b5dc5b8f42ef480a1a9b8b09ab6e60861b68ca50be6c7ae370eaf5ffc2bf3
PageCount 8
ParticipantIDs ieee_primary_9827285
PublicationCentury 2000
PublicationDate 2022-June-5
PublicationDateYYYYMMDD 2022-06-05
PublicationDate_xml – month: 06
  year: 2022
  text: 2022-June-5
  day: 05
PublicationDecade 2020
PublicationTitle 2022 IEEE Intelligent Vehicles Symposium (IV)
PublicationTitleAbbrev IV
PublicationYear 2022
Publisher IEEE
Publisher_xml – name: IEEE
Score 2.3325396
Snippet Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However,...
SourceID ieee
SourceType Publisher
StartPage 1731
SubjectTerms Anomaly Detection
Automotive Ethernet
AVTP
Deep learning
Ethernet
In-Vehicle Network
Network intrusion detection
Neural Network
Numerical models
Real-time systems
Telecommunication traffic
Transport protocols
Title Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks
URI https://ieeexplore.ieee.org/document/9827285
WOSCitedRecordID wos000854106700245&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Pb8IgFCZqdthpW3TZ73DYcVUKhcLRbJp5MR7UeDOlPDIvaNbWv3-Fdi5LdtmNAIHkeyHvPfg-HkLPTIFgiTSRpUpEiZAq0hx4xIEZmXEOMZBQbCKdz-VmoxYd9HLSwgBAIJ_B0DfDW77Z55W_KhspSVMqeRd101Q0Wq1WDRcTNZqtvQjT53yUDtupv2qmBJcxvfjfZpdo8KO9w4uTV7lCHXB9tFm5ojr4Y12AwfOGuo1nzismamDxG5SBUuVw8wM5rkNRPF4vF3jn8LgqA-XuCHjiwz0H5fcSxQCtppPl63vU1kSIPihLywiYEJqbnGtpEwo2kSSLM6WlJirTAkSdosRaSF_pQIPI0wxYSiCz3NqcasuuUc_tHdwgLARhhhJO69FEGJBaWJKDiZWpLUXELep7VLaH5tuLbQvI3d_d9-jcAx9YVPwB9WoA4BGd5cdyV3w-BVt9AbedmdM
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwFG4QTfSkBoy_7cGjg65du_ZIFAIRCQcg3Mi6vkYvg7jB3-_aTYyJF29N27TJ99K81_b73kPokSkQLJImsFSJIBJSBZoDDzgwIxPOIQTii03Ek4lcLtW0gZ72WhgA8OQz6Lim_8s363Trnsq6StKYSn6ADnkUUVKptWo9XEhUd7RwMkx366O0U0_-VTXFO43B6f-2O0PtH_Udnu79yjlqQNZCy3mWbzfuYOdg8KQib-NR5jQTJbT4BQpPqspwlYMcl8Eo7i1mU_yR4d628KS7HeC-C_gyKL6XyNtoPujPnodBXRUheKcsLgJgQmhuUq6ljSjYSJIkTJSWmqhECxDlJSXUQrpaBxpEGifAYgKJ5damVFt2gZrZOoNLhIUgzFDCaTkaCQNSC0tSMKEypa2IuEIth8pqUyW-WNWAXP_d_YCOh7O38Wo8mrzeoBNnBM-p4reoWYIBd-go3RUf-ee9t9sX4w2dGg
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2022+IEEE+Intelligent+Vehicles+Symposium+%28IV%29&rft.atitle=Unsupervised+Network+Intrusion+Detection+System+for+AVTP+in+Automotive+Ethernet+Networks&rft.au=Alkhatib%2C+Natasha&rft.au=Mushtaq%2C+Maria&rft.au=Ghauch%2C+Hadi&rft.au=Danger%2C+Jean-Luc&rft.date=2022-06-05&rft.pub=IEEE&rft.spage=1731&rft.epage=1738&rft_id=info:doi/10.1109%2FIV51971.2022.9827285&rft.externalDocID=9827285