The severity and effects of Cyber-breaches in SMEs: a machine learning approach

The severity and effects of Cyber-breaches in SMEs: a machine learning approach

In this paper, we investigate cyber breaches and their effects on small and medium enterprises (SMEs), considering the role that cybersecurity plays in SMEs, and the importance that SMEs have in the economy. Using the Cyber Security Breaches Survey data, the first contribution extends previous works...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Enterprise information systems 2023-03, Vol.17 (3)
Hauptverfasser: Fernandez De Arroyabe, Ignacio, Fernandez de Arroyabe, Juan Carlos
Format: Artikel
Sprache:eng
Schlagworte:
artificial neural network
Cyber breaches
effects
severity
smes
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 3
container_start_page
container_title Enterprise information systems
container_volume 17
creator Fernandez De Arroyabe, Ignacio
Fernandez de Arroyabe, Juan Carlos
description In this paper, we investigate cyber breaches and their effects on small and medium enterprises (SMEs), considering the role that cybersecurity plays in SMEs, and the importance that SMEs have in the economy. Using the Cyber Security Breaches Survey data, the first contribution extends previous works confirming that SMEs receive a wide variety of breaches. Secondly, we have characterized the degree of severity of breaches in SMEs, based on disruption time and their cost. Our last contribution consists of determining the effect and severity of breaches in SMEs in terms of economic, financial and management impacts.
doi_str_mv 10.1080/17517575.2021.1942997
format Article
fullrecord <record><control><sourceid>crossref_infor</sourceid><recordid>TN_cdi_crossref_primary_10_1080_17517575_2021_1942997</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_1080_17517575_2021_1942997</sourcerecordid><originalsourceid>FETCH-LOGICAL-c357t-80706ed8a9519c04f5281684db1e7a840440c97d1935504dc0d43d76b7e849393</originalsourceid><addsrcrecordid>eNp9kNFKwzAUhoMoOKePIOQFOpMmaRKvlDGnMNmF8zqkyYmrdOlIitK3t2PTS-HAOXzw_wc-hG4pmVGiyB2VYhwpZiUp6YxqXmotz9DkwAspFDv_u6W4RFc5fxJSKSLFBK03W8AZviA1_YBt9BhCANdn3AU8H2pIRZ3Aui1k3ET89rrI99ji3UiaCLgFm2ITP7Dd71M3wmt0EWyb4ea0p-j9abGZPxer9fJl_rgqHBOyL8bnpAKvrBZUO8KDKBWtFPc1BWkVJ5wTp6WnmglBuHfEc-ZlVUtQXDPNpkgce13qck4QzD41O5sGQ4k5WDG_VszBijlZGXMPx1wTQ5d29rtLrTe9HdouhWSja7Jh_1f8AMQ1Z6A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>The severity and effects of Cyber-breaches in SMEs: a machine learning approach</title><source>Business Source Complete</source><creator>Fernandez De Arroyabe, Ignacio ; Fernandez de Arroyabe, Juan Carlos</creator><creatorcontrib>Fernandez De Arroyabe, Ignacio ; Fernandez de Arroyabe, Juan Carlos</creatorcontrib><description>In this paper, we investigate cyber breaches and their effects on small and medium enterprises (SMEs), considering the role that cybersecurity plays in SMEs, and the importance that SMEs have in the economy. Using the Cyber Security Breaches Survey data, the first contribution extends previous works confirming that SMEs receive a wide variety of breaches. Secondly, we have characterized the degree of severity of breaches in SMEs, based on disruption time and their cost. Our last contribution consists of determining the effect and severity of breaches in SMEs in terms of economic, financial and management impacts.</description><identifier>ISSN: 1751-7575</identifier><identifier>EISSN: 1751-7583</identifier><identifier>DOI: 10.1080/17517575.2021.1942997</identifier><language>eng</language><publisher>Taylor &amp; Francis</publisher><subject>artificial neural network ; Cyber breaches ; effects ; severity ; smes</subject><ispartof>Enterprise information systems, 2023-03, Vol.17 (3)</ispartof><rights>2021 The Author(s). Published by Informa UK Limited, trading as Taylor &amp; Francis Group. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c357t-80706ed8a9519c04f5281684db1e7a840440c97d1935504dc0d43d76b7e849393</citedby><cites>FETCH-LOGICAL-c357t-80706ed8a9519c04f5281684db1e7a840440c97d1935504dc0d43d76b7e849393</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Fernandez De Arroyabe, Ignacio</creatorcontrib><creatorcontrib>Fernandez de Arroyabe, Juan Carlos</creatorcontrib><title>The severity and effects of Cyber-breaches in SMEs: a machine learning approach</title><title>Enterprise information systems</title><description>In this paper, we investigate cyber breaches and their effects on small and medium enterprises (SMEs), considering the role that cybersecurity plays in SMEs, and the importance that SMEs have in the economy. Using the Cyber Security Breaches Survey data, the first contribution extends previous works confirming that SMEs receive a wide variety of breaches. Secondly, we have characterized the degree of severity of breaches in SMEs, based on disruption time and their cost. Our last contribution consists of determining the effect and severity of breaches in SMEs in terms of economic, financial and management impacts.</description><subject>artificial neural network</subject><subject>Cyber breaches</subject><subject>effects</subject><subject>severity</subject><subject>smes</subject><issn>1751-7575</issn><issn>1751-7583</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>0YH</sourceid><recordid>eNp9kNFKwzAUhoMoOKePIOQFOpMmaRKvlDGnMNmF8zqkyYmrdOlIitK3t2PTS-HAOXzw_wc-hG4pmVGiyB2VYhwpZiUp6YxqXmotz9DkwAspFDv_u6W4RFc5fxJSKSLFBK03W8AZviA1_YBt9BhCANdn3AU8H2pIRZ3Aui1k3ET89rrI99ji3UiaCLgFm2ITP7Dd71M3wmt0EWyb4ea0p-j9abGZPxer9fJl_rgqHBOyL8bnpAKvrBZUO8KDKBWtFPc1BWkVJ5wTp6WnmglBuHfEc-ZlVUtQXDPNpkgce13qck4QzD41O5sGQ4k5WDG_VszBijlZGXMPx1wTQ5d29rtLrTe9HdouhWSja7Jh_1f8AMQ1Z6A</recordid><startdate>20230304</startdate><enddate>20230304</enddate><creator>Fernandez De Arroyabe, Ignacio</creator><creator>Fernandez de Arroyabe, Juan Carlos</creator><general>Taylor &amp; Francis</general><scope>0YH</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20230304</creationdate><title>The severity and effects of Cyber-breaches in SMEs: a machine learning approach</title><author>Fernandez De Arroyabe, Ignacio ; Fernandez de Arroyabe, Juan Carlos</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c357t-80706ed8a9519c04f5281684db1e7a840440c97d1935504dc0d43d76b7e849393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>artificial neural network</topic><topic>Cyber breaches</topic><topic>effects</topic><topic>severity</topic><topic>smes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fernandez De Arroyabe, Ignacio</creatorcontrib><creatorcontrib>Fernandez de Arroyabe, Juan Carlos</creatorcontrib><collection>Taylor &amp; Francis Open Access</collection><collection>CrossRef</collection><jtitle>Enterprise information systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fernandez De Arroyabe, Ignacio</au><au>Fernandez de Arroyabe, Juan Carlos</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The severity and effects of Cyber-breaches in SMEs: a machine learning approach</atitle><jtitle>Enterprise information systems</jtitle><date>2023-03-04</date><risdate>2023</risdate><volume>17</volume><issue>3</issue><issn>1751-7575</issn><eissn>1751-7583</eissn><abstract>In this paper, we investigate cyber breaches and their effects on small and medium enterprises (SMEs), considering the role that cybersecurity plays in SMEs, and the importance that SMEs have in the economy. Using the Cyber Security Breaches Survey data, the first contribution extends previous works confirming that SMEs receive a wide variety of breaches. Secondly, we have characterized the degree of severity of breaches in SMEs, based on disruption time and their cost. Our last contribution consists of determining the effect and severity of breaches in SMEs in terms of economic, financial and management impacts.</abstract><pub>Taylor &amp; Francis</pub><doi>10.1080/17517575.2021.1942997</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1751-7575
ispartof Enterprise information systems, 2023-03, Vol.17 (3)
issn 1751-7575
1751-7583
language eng
recordid cdi_crossref_primary_10_1080_17517575_2021_1942997
source Business Source Complete
subjects artificial neural network
Cyber breaches
effects
severity
smes
title The severity and effects of Cyber-breaches in SMEs: a machine learning approach
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T20%3A20%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref_infor&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20severity%20and%20effects%20of%20Cyber-breaches%20in%20SMEs:%20a%20machine%20learning%20approach&rft.jtitle=Enterprise%20information%20systems&rft.au=Fernandez%20De%20Arroyabe,%20Ignacio&rft.date=2023-03-04&rft.volume=17&rft.issue=3&rft.issn=1751-7575&rft.eissn=1751-7583&rft_id=info:doi/10.1080/17517575.2021.1942997&rft_dat=%3Ccrossref_infor%3E10_1080_17517575_2021_1942997%3C/crossref_infor%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true