{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:35:03Z","timestamp":1750221303623,"version":"3.41.0"},"reference-count":12,"publisher":"Association for Computing Machinery (ACM)","issue":"July","license":[{"start":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T00:00:00Z","timestamp":1532390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Ubiquity"],"published-print":{"date-parts":[[2018,7,26]]},"abstract":"<jats:p>In modern times, we have seen a major shift toward hybrid cloud architectures, where corporations operate in a large, highly extended eco-system. Thus, the traditional enterprise security perimeter is disappearing and evolving into the concept of security intelligence where the volume, velocity\/rate, and variety of data have dramatically changed. Today, to cope with the fast-changing security landscape, we need to be able to transform huge data lakes via security analytics and big data technologies into effective security intelligence presented through a security \"cockpit\" to achieve a better corporate security and compliance level, support sound risk management and informed decision making. We present a high-level architecture for efficient security intelligence and the concept of a security cockpit as a point of control for the corporate security and compliance state. Therefore, we could conclude nowadays corporate security can be perceived as a big-data problem.<\/jats:p>","DOI":"10.1145\/3158348","type":"journal-article","created":{"date-parts":[[2018,7,26]],"date-time":"2018-07-26T11:58:04Z","timestamp":1532606284000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Corporate Security is a Big Data Problem"],"prefix":"10.1145","volume":"2018","author":[{"given":"Louisa","family":"Saunier","sequence":"first","affiliation":[]},{"given":"Kemal A.","family":"Delic","sequence":"additional","affiliation":[]}],"member":"320","published-online":{"date-parts":[[2018,7,24]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"CISCO. The Zettabyte era: trends and analysis. June 7 2017.  CISCO. The Zettabyte era: trends and analysis. June 7 2017."},{"key":"e_1_2_1_2_1","unstructured":"Gemalto. 2015 First Half Review: Findings from the Breach Level Index. 2015;  Gemalto. 2015 First Half Review: Findings from the Breach Level Index. 2015;"},{"key":"e_1_2_1_3_1","unstructured":"Protenus Inc. 2016 Breach Barometer Report: Year In Review. 2017.  Protenus Inc. 2016 Breach Barometer Report: Year In Review. 2017."},{"key":"e_1_2_1_4_1","unstructured":"Cloud Security Alliance (CSA) Big Data Working Group. Big Data Analytics for Security Intelligence. Report. September 2013.  Cloud Security Alliance (CSA) Big Data Working Group. Big Data Analytics for Security Intelligence. Report. September 2013."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS.2011.6123125"},{"key":"e_1_2_1_6_1","unstructured":"Buriya S. Bhilare D. S. and Singh A. A survey of botclouds: Botnet detection using MapReduce and big data analytics. 2015.  Buriya S. Bhilare D. S. and Singh A. A survey of botclouds: Botnet detection using MapReduce and big data analytics. 2015."},{"key":"e_1_2_1_7_1","unstructured":"Giura P. and W. Wang. Using large scale distributed computing to unveil advanced persistent threats Academy of Science and Engineering (ASE) Science Journal 1 3 (2012) 93-105.  Giura P. and W. Wang. Using large scale distributed computing to unveil advanced persistent threats Academy of Science and Engineering (ASE) Science Journal 1 3 (2012) 93-105."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1978672.1978683"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Las-Casas P. H. B. Santos Dias V. Meira W. Jr. and Guedes D. A big data architecture for security data and its application to phishing characterization. In Proceedings of IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity) IEEE International Conference on High Performance and Smart Computing (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS). IEEE Washington D.C. 2016.  Las-Casas P. H. B. Santos Dias V. Meira W. Jr. and Guedes D. A big data architecture for security data and its application to phishing characterization. In Proceedings of IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity) IEEE International Conference on High Performance and Smart Computing (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS) . 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Data for cybersecurity research: Process and \"wish list.\" 2009."}],"container-title":["Ubiquity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3158348","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3158348","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:13:24Z","timestamp":1750212804000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3158348"}},"subtitle":["Big Data (Ubiquity symposium)"],"short-title":[],"issued":{"date-parts":[[2018,7,24]]},"references-count":12,"journal-issue":{"issue":"July","published-print":{"date-parts":[[2018,7,26]]}},"alternative-id":["10.1145\/3158348"],"URL":"https:\/\/doi.org\/10.1145\/3158348","relation":{},"ISSN":["1530-2180"],"issn-type":[{"type":"electronic","value":"1530-2180"}],"subject":[],"published":{"date-parts":[[2018,7,24]]},"assertion":[{"value":"2018-07-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}