Loading…
Intrusion detection in connected cars
Automobiles are becoming increasingly connected and therefore are vulnerable to cyber attacks. Intrusion detection systems are a well established technique in cyber security and are now being deployed to detect attacks on cars by filtering the data streams which are exchanged by the car and the outs...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | 519 |
container_issue | |
container_start_page | 516 |
container_title | |
container_volume | |
creator | Haas, Roland E. Moller, Dietmar P. F. Bansal, Prateek Ghosh, Rahul Bhat, Srikrishna S. |
description | Automobiles are becoming increasingly connected and therefore are vulnerable to cyber attacks. Intrusion detection systems are a well established technique in cyber security and are now being deployed to detect attacks on cars by filtering the data streams which are exchanged by the car and the outside world as well within the automotive bus systems. The paper gives a brief overview of intrusion detection systems and discusses some of the major cyber security threats arising in the world of connected cars. We also show how intrusion detection systems can be implemented by artificial neural networks. |
doi_str_mv | 10.1109/EIT.2017.8053416 |
format | conference_proceeding |
fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_8053416</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8053416</ieee_id><sourcerecordid>8053416</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-5df269154321c0101308d4d37767455556bc4f7759450f30cbf693f48a52b8073</originalsourceid><addsrcrecordid>eNotTz1LA0EUXIWAIV4v2Fxjeed7-_azlBD1IGCT1OFuP2BFN3J7Fv57V8w086aYmTeM3SH0iGAfd8Oh54C6NyBJoLpijdUGJVgQWmm6ZmuOUnRAmm5YU8o7AFSjstys2cOQl_m7pHNufViCW_6ulFt3zrmq4Fs3zuWWreL4UUJz4Q07Pu8O29du__YybJ_2XUItl076yJWtZcTR1Q4kMF540vUPISvU5ETUWlohIRK4KSpLUZhR8smApg27_89NIYTT15w-x_nndBlGvyumPj0</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Intrusion detection in connected cars</title><source>IEEE Xplore All Conference Series</source><creator>Haas, Roland E. ; Moller, Dietmar P. F. ; Bansal, Prateek ; Ghosh, Rahul ; Bhat, Srikrishna S.</creator><creatorcontrib>Haas, Roland E. ; Moller, Dietmar P. F. ; Bansal, Prateek ; Ghosh, Rahul ; Bhat, Srikrishna S.</creatorcontrib><description>Automobiles are becoming increasingly connected and therefore are vulnerable to cyber attacks. Intrusion detection systems are a well established technique in cyber security and are now being deployed to detect attacks on cars by filtering the data streams which are exchanged by the car and the outside world as well within the automotive bus systems. The paper gives a brief overview of intrusion detection systems and discusses some of the major cyber security threats arising in the world of connected cars. We also show how intrusion detection systems can be implemented by artificial neural networks.</description><identifier>EISSN: 2154-0373</identifier><identifier>EISBN: 9781509047673</identifier><identifier>EISBN: 1509047670</identifier><identifier>DOI: 10.1109/EIT.2017.8053416</identifier><language>eng</language><publisher>IEEE</publisher><subject>Automobiles ; Computer crime ; Delays ; Intrusion detection ; Neurons</subject><ispartof>2017 IEEE International Conference on Electro Information Technology (EIT), 2017, p.516-519</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8053416$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27902,54530,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8053416$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Haas, Roland E.</creatorcontrib><creatorcontrib>Moller, Dietmar P. F.</creatorcontrib><creatorcontrib>Bansal, Prateek</creatorcontrib><creatorcontrib>Ghosh, Rahul</creatorcontrib><creatorcontrib>Bhat, Srikrishna S.</creatorcontrib><title>Intrusion detection in connected cars</title><title>2017 IEEE International Conference on Electro Information Technology (EIT)</title><addtitle>EIT</addtitle><description>Automobiles are becoming increasingly connected and therefore are vulnerable to cyber attacks. Intrusion detection systems are a well established technique in cyber security and are now being deployed to detect attacks on cars by filtering the data streams which are exchanged by the car and the outside world as well within the automotive bus systems. The paper gives a brief overview of intrusion detection systems and discusses some of the major cyber security threats arising in the world of connected cars. We also show how intrusion detection systems can be implemented by artificial neural networks.</description><subject>Automobiles</subject><subject>Computer crime</subject><subject>Delays</subject><subject>Intrusion detection</subject><subject>Neurons</subject><issn>2154-0373</issn><isbn>9781509047673</isbn><isbn>1509047670</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2017</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotTz1LA0EUXIWAIV4v2Fxjeed7-_azlBD1IGCT1OFuP2BFN3J7Fv57V8w086aYmTeM3SH0iGAfd8Oh54C6NyBJoLpijdUGJVgQWmm6ZmuOUnRAmm5YU8o7AFSjstys2cOQl_m7pHNufViCW_6ulFt3zrmq4Fs3zuWWreL4UUJz4Q07Pu8O29du__YybJ_2XUItl076yJWtZcTR1Q4kMF540vUPISvU5ETUWlohIRK4KSpLUZhR8smApg27_89NIYTT15w-x_nndBlGvyumPj0</recordid><startdate>201705</startdate><enddate>201705</enddate><creator>Haas, Roland E.</creator><creator>Moller, Dietmar P. F.</creator><creator>Bansal, Prateek</creator><creator>Ghosh, Rahul</creator><creator>Bhat, Srikrishna S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201705</creationdate><title>Intrusion detection in connected cars</title><author>Haas, Roland E. ; Moller, Dietmar P. F. ; Bansal, Prateek ; Ghosh, Rahul ; Bhat, Srikrishna S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-5df269154321c0101308d4d37767455556bc4f7759450f30cbf693f48a52b8073</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Automobiles</topic><topic>Computer crime</topic><topic>Delays</topic><topic>Intrusion detection</topic><topic>Neurons</topic><toplevel>online_resources</toplevel><creatorcontrib>Haas, Roland E.</creatorcontrib><creatorcontrib>Moller, Dietmar P. F.</creatorcontrib><creatorcontrib>Bansal, Prateek</creatorcontrib><creatorcontrib>Ghosh, Rahul</creatorcontrib><creatorcontrib>Bhat, Srikrishna S.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Haas, Roland E.</au><au>Moller, Dietmar P. F.</au><au>Bansal, Prateek</au><au>Ghosh, Rahul</au><au>Bhat, Srikrishna S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Intrusion detection in connected cars</atitle><btitle>2017 IEEE International Conference on Electro Information Technology (EIT)</btitle><stitle>EIT</stitle><date>2017-05</date><risdate>2017</risdate><spage>516</spage><epage>519</epage><pages>516-519</pages><eissn>2154-0373</eissn><eisbn>9781509047673</eisbn><eisbn>1509047670</eisbn><abstract>Automobiles are becoming increasingly connected and therefore are vulnerable to cyber attacks. Intrusion detection systems are a well established technique in cyber security and are now being deployed to detect attacks on cars by filtering the data streams which are exchanged by the car and the outside world as well within the automotive bus systems. The paper gives a brief overview of intrusion detection systems and discusses some of the major cyber security threats arising in the world of connected cars. We also show how intrusion detection systems can be implemented by artificial neural networks.</abstract><pub>IEEE</pub><doi>10.1109/EIT.2017.8053416</doi><tpages>4</tpages></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | EISSN: 2154-0373 |
ispartof | 2017 IEEE International Conference on Electro Information Technology (EIT), 2017, p.516-519 |
issn | 2154-0373 |
language | eng |
recordid | cdi_ieee_primary_8053416 |
source | IEEE Xplore All Conference Series |
subjects | Automobiles Computer crime Delays Intrusion detection Neurons |
title | Intrusion detection in connected cars |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T21%3A18%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Intrusion%20detection%20in%20connected%20cars&rft.btitle=2017%20IEEE%20International%20Conference%20on%20Electro%20Information%20Technology%20(EIT)&rft.au=Haas,%20Roland%20E.&rft.date=2017-05&rft.spage=516&rft.epage=519&rft.pages=516-519&rft.eissn=2154-0373&rft_id=info:doi/10.1109/EIT.2017.8053416&rft.eisbn=9781509047673&rft.eisbn_list=1509047670&rft_dat=%3Cieee_CHZPO%3E8053416%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-5df269154321c0101308d4d37767455556bc4f7759450f30cbf693f48a52b8073%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=8053416&rfr_iscdi=true |