{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T08:32:32Z","timestamp":1776501152945,"version":"3.51.2"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T00:00:00Z","timestamp":1637107200000},"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":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2022,7,31]]},"abstract":"<jats:p>\n            Mobile advertising has undoubtedly become one of the fastest-growing industries in the world. The influx of capital attracts increasing fraudsters to defraud money from advertisers. Fraudsters can leverage many techniques, where bots install fraud is the most difficult to detect due to its ability to emulate normal users by implementing sophisticated behavioral patterns to evade from detection rules defined by human experts. Therefore, we proposed BotSpot\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n            for bots install fraud detection previously. However, there are some drawbacks in BotSpot, such as the sparsity of the devices\u2019 neighbors, weak interactive information of leaf nodes, and noisy labels. In this work, we propose BotSpot++ to improve these drawbacks: (1) for the sparsity of the devices\u2019 neighbors, we propose to construct a super device node to enrich the graph structure and information flow utilizing domain knowledge and a clustering algorithm; (2) for the weak interactive information, we propose to incorporate a self-attention mechanism to enhance the interaction of various leaf nodes; and (3) for the noisy labels, we apply a label smoothing mechanism to alleviate it. Comprehensive experimental results show that BotSpot++ yields the best performance compared with six state-of-the-art baselines. Furthermore, we deploy our model to the advertising platform of Mobvista,\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>2<\/jats:sup>\n            <\/jats:xref>\n            a leading global mobile advertising company. The online experiments also demonstrate the effectiveness of our proposed method.\n          <\/jats:p>","DOI":"10.1145\/3476107","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T21:53:46Z","timestamp":1637186026000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["BotSpot++: A Hierarchical Deep Ensemble Model for Bots Install Fraud Detection in Mobile Advertising"],"prefix":"10.1145","volume":"40","author":[{"given":"Yadong","family":"Zhu","sequence":"first","affiliation":[{"name":"Mobvista Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Mobvista Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"Mobvista Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianjun","family":"Yao","sequence":"additional","affiliation":[{"name":"Mobvista Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangsong","family":"Liang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"2019 Fraud Trends Uncover Fascinating Results","year":"2019","unstructured":"AppsFlyer. 2019. 2019 Fraud Trends Uncover Fascinating Results. 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