{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:44:26Z","timestamp":1740149066470,"version":"3.37.3"},"reference-count":42,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T00:00:00Z","timestamp":1623110400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&D Program of China","award":["2017YFB0802803","4202002","2019JSJKY004"],"award-info":[{"award-number":["2017YFB0802803","4202002","2019JSJKY004"]}]},{"DOI":"10.13039\/501100005089","name":"Beijing Municipal Natural Science Foundation","doi-asserted-by":"publisher","award":["2017YFB0802803","4202002","2019JSJKY004"],"award-info":[{"award-number":["2017YFB0802803","4202002","2019JSJKY004"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003444","name":"Beijing University of Technology","doi-asserted-by":"publisher","award":["2017YFB0802803","4202002","2019JSJKY004"],"award-info":[{"award-number":["2017YFB0802803","4202002","2019JSJKY004"]}],"id":[{"id":"10.13039\/501100003444","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,6,8]]},"abstract":"<jats:p>With the rapid development of the Internet since the beginning of the 21st century, social networks have provided a significant amount of convenience for work, study, and entertainment. Specifically, because of the irreplaceable superiority of social platforms in disseminating information, criminals have thus updated the main methods of social engineering attacks. Detecting abnormal accounts on social networks in a timely manner can effectively prevent the occurrence of malicious Internet events. Different from previous research work, in this work, a method of anomaly detection called Hurst of Interest Distribution is proposed based on the stability of user interest quantifiable from the content of users\u2019 tweets, so as to detect abnormal accounts. In detail, the Latent Dirichlet Allocation model is adopted to classify blog content on Twitter into topics to calculate and obtain the topic distribution of tweets sent by a single user within a period of time. Then, the stability degree of the user\u2019s tweet topic preference is calculated according to the Hurst index to determine whether the account is compromised. Through experiments, the Hurst indexes of normal and abnormal accounts are found to be significantly different, and the detection rate of abnormal accounts using the proposed method can reach up to 97.93%.<\/jats:p>","DOI":"10.1155\/2021\/6653430","type":"journal-article","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T18:35:14Z","timestamp":1623263714000},"page":"1-14","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Abnormal Social Network Accounts with Hurst of Interest Distribution"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1520-9053","authenticated-orcid":true,"given":"Xiujuan","family":"Wang","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9099-8980","authenticated-orcid":true,"given":"Yi","family":"Sui","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3210-5256","authenticated-orcid":true,"given":"Yuanrui","family":"Tao","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7528-4229","authenticated-orcid":true,"given":"Qianqian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}]},{"given":"Jianhua","family":"Wei","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"}]}],"member":"311","reference":[{"year":"2021","key":"1","article-title":"Digital 2021: global overview report"},{"year":"2021","key":"2","article-title":"Spam and phishing in 2020"},{"issue":"4","key":"3","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1080\/0960085X.2018.1560920","article-title":"Are social bots a real threat? 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