{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T06:09:00Z","timestamp":1776406140211,"version":"3.51.2"},"reference-count":15,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T00:00:00Z","timestamp":1531872000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science, ICT","award":["IITP-2018-2015-0-00403"],"award-info":[{"award-number":["IITP-2018-2015-0-00403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2018,7,18]]},"abstract":"<jats:p>In the era of Internet of Things (IoT), impact of social media is increasing gradually. With the huge progress in the IoT device, insider threat is becoming much more dangerous. Trying to find what kind of people are in high risk for the organization, about one million of tweets were analyzed by sentiment analysis methodology. Dataset made by the web service \u201cSentiment140\u201d was used to find possible malicious insider. Based on the analysis of the sentiment level, users with negative sentiments were classified by the criteria and then selected as possible malicious insiders according to the threat level. Machine learning algorithms in the open-sourced machine learning software \u201cWeka (Waikato Environment for Knowledge Analysis)\u201d were used to find the possible malicious insider. Decision Tree had the highest accuracy among supervised learning algorithms and K-Means had the highest accuracy among unsupervised learning. In addition, we extract the frequently used words from the topic modeling technique and then verified the analysis results by matching them to the information security compliance elements. These findings can contribute to achieve higher detection accuracy by combining individual\u2019s characteristics to the previous studies such as analyzing system behavior.<\/jats:p>","DOI":"10.1155\/2018\/7243296","type":"journal-article","created":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T21:00:16Z","timestamp":1531947616000},"page":"1-8","source":"Crossref","is-referenced-by-count":27,"title":["Detecting Potential Insider Threat: Analyzing Insiders\u2019 Sentiment Exposed in Social Media"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7643-388X","authenticated-orcid":true,"given":"Won","family":"Park","sequence":"first","affiliation":[{"name":"Institute of Cyber Security & Privacy, Korea University, Seoul 02841, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1214-9596","authenticated-orcid":true,"given":"Youngin","family":"You","sequence":"additional","affiliation":[{"name":"Institute of Cyber Security & Privacy, Korea University, Seoul 02841, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5183-5927","authenticated-orcid":true,"given":"Kyungho","family":"Lee","sequence":"additional","affiliation":[{"name":"Institute of Cyber Security & Privacy, Korea University, Seoul 02841, Republic of Korea"}]}],"member":"311","reference":[{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2014.2318835"},{"issue":"3","key":"5","volume":"9","year":"2017","journal-title":"Future Internet"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.istr.2010.04.004"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2015.09.009"},{"key":"9","first-page":"69","volume":"39","year":"2008","journal-title":"Advances in Information Security"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1504\/IJSN.2017.10005217"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1145\/2133806.2133826"},{"key":"22","year":"2014"},{"key":"23","first-page":"137","volume":"1398","year":"1998","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface"},{"key":"24","year":"2010"},{"key":"25","first-page":"1","journal-title":"Microsoft Research"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1109\/79.543975"},{"key":"28","journal-title":"Research Briefs on Information & Communication Technology Evolution"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1177\/0002764203260208"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0142390"}],"container-title":["Security and Communication Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2018\/7243296.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2018\/7243296.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2018\/7243296.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T21:00:21Z","timestamp":1531947621000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/scn\/2018\/7243296\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,18]]},"references-count":15,"alternative-id":["7243296","7243296"],"URL":"https:\/\/doi.org\/10.1155\/2018\/7243296","relation":{},"ISSN":["1939-0114","1939-0122"],"issn-type":[{"value":"1939-0114","type":"print"},{"value":"1939-0122","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,18]]}}}