{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:31:08Z","timestamp":1765369868742,"version":"3.37.3"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T00:00:00Z","timestamp":1633910400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T00:00:00Z","timestamp":1633910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s11042-021-11533-4","type":"journal-article","created":{"date-parts":[[2021,10,12]],"date-time":"2021-10-12T02:57:38Z","timestamp":1634007458000},"page":"1685-1718","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A hybrid approach for identifying non-human traffic in online digital advertising"],"prefix":"10.1007","volume":"81","author":[{"given":"Sawsan","family":"Almahmoud","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bassam","family":"Hammo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5214-6429","authenticated-orcid":false,"given":"Bashar","family":"Al-Shboul","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadim","family":"Obeid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,11]]},"reference":[{"key":"11533_CR1","unstructured":"Alexopoulos P, Kafentzis K, Benetou X, Tagaris T, Georgolios P (2007) Towards a generic fraud ontology in e-government. In\u00a0ICE-B 269\u2013276"},{"key":"11533_CR2","doi-asserted-by":"publisher","first-page":"285","DOI":"10.4324\/9781315623252-16","volume-title":"Digital Advertising","author":"S Alhabash","year":"2017","unstructured":"Alhabash S, Mundel J, Hussain SA (2017) Social media advertising: unraveling the mystery box. Digital Advertising. Routledge, England, pp 285\u2013299"},{"key":"11533_CR3","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.future.2019.03.041","volume":"100","author":"MA Ali","year":"2019","unstructured":"Ali MA, Azad MA, Centeno MP, Hao F, van Moorsel A (2019) Consumer-facing technology fraud: Economics, attack methods and potential solutions. Futur Gener Comput Syst 100:408\u2013427. https:\/\/doi.org\/10.1016\/j.future.2019.03.041","journal-title":"Futur Gener Comput Syst"},{"key":"11533_CR4","doi-asserted-by":"publisher","unstructured":"Almahmoud S, Hammo B, Al-Shboul B (2019) Exploring non-human traffic in online digital advertisements: analysis and prediction. In: International Conference on Computational Collective Intelligence. Springer, Cham. pp. 663\u2013675. https:\/\/doi.org\/10.1007\/978-3-030-28374-2_57","DOI":"10.1007\/978-3-030-28374-2_57"},{"key":"11533_CR5","doi-asserted-by":"publisher","unstructured":"Alrwais SA, Gerber A, Dunn CW, Spatscheck O, Gupta M, Osterweil E (2012) Dissecting ghost clicks: Ad fraud via misdirected human clicks. In: Proceedings of the 28th Annual Computer Security Applications Conference pp. 21\u201330. https:\/\/doi.org\/10.1145\/2420950.2420954","DOI":"10.1145\/2420950.2420954"},{"issue":"3","key":"11533_CR6","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","volume":"46","author":"NS Altman","year":"1992","unstructured":"Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175\u2013185. https:\/\/doi.org\/10.1080\/00031305.1992.10475879","journal-title":"Am Stat"},{"key":"11533_CR7","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.procs.2018.08.186","volume":"135","author":"G Attigeri","year":"2018","unstructured":"Attigeri G, MM MP, Pai RM, Kulkarni R (2018) Knowledge base ontology building for fraud detection using topic modeling. Procedia Comput Sci 135:369\u2013376","journal-title":"Procedia Comput Sci"},{"key":"11533_CR8","unstructured":"Baarder F, Nutt W (2003) The description logic handbook, chapter 2. Basic description logics. pp  43\u201395"},{"volume-title":"The description logic handbook: theory, implementation and applications","year":"2003","key":"11533_CR9","unstructured":"Baader F, Calvanese D, McGuinness D, Patel-Schneider P, Nardi D (eds) (2003) The description logic handbook: theory, implementation and applications. Cambridge University Press, Cambridge"},{"key":"11533_CR10","doi-asserted-by":"publisher","unstructured":"Boser BE, Guyon I.M, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory.  pp 144\u2013152. https:\/\/doi.org\/10.1145\/130385.130401","DOI":"10.1145\/130385.130401"},{"issue":"2","key":"11533_CR11","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140. https:\/\/doi.org\/10.1007\/BF00058655","journal-title":"Mach Learn"},{"key":"11533_CR12","doi-asserted-by":"publisher","unstructured":"Buehrer G, Stokes JW, Chellapilla K (2008) A large-scale study of automated web search traffic. In: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web. pp 1\u20138. https:\/\/doi.org\/10.1145\/1451983.1451985","DOI":"10.1145\/1451983.1451985"},{"key":"11533_CR13","first-page":"19","volume-title":"Uncertainty reasoning for the semantic web ii","author":"RN Carvalho","year":"2010","unstructured":"Carvalho RN, Matsumoto S, Laskey KB, Costa PC, Ladeira M, Santos LL (2010) Probabilistic ontology and knowledge fusion for procurement fraud detection in Brazil. Uncertainty reasoning for the semantic web ii. Springer, Berlin, Heidelberg, pp 19\u201340"},{"issue":"6","key":"11533_CR14","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1016\/j.ipm.2016.04.009","volume":"52","author":"M Chakraborty","year":"2016","unstructured":"Chakraborty M, Pal S, Pramanik R, Chowdary CR (2016) Recent developments in social spam detection and combating techniques: a survey. Inf Process Manag 52(6):1053\u20131073. https:\/\/doi.org\/10.1016\/j.ipm.2016.04.009","journal-title":"Inf Process Manag"},{"key":"11533_CR15","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.cose.2017.02.010","volume":"67","author":"Y Chen","year":"2017","unstructured":"Chen Y, Kintis P, Antonakakis M, Nadji Y, Dagon D, Farrell M (2017) Measuring lower bounds of the financial abuse to online advertisers: a four year case study of the TDSS\/TDL4 Botnet. Comput Secur 67:164\u2013180. https:\/\/doi.org\/10.1016\/j.cose.2017.02.010","journal-title":"Comput Secur"},{"key":"11533_CR16","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"11533_CR17","unstructured":"Digital Ad Primer [online]. Available: https:\/\/www.martechadvisor.com\/articles\/display-and-native-advertising\/digital-advertising-primer-martech-101\/, Accessed from 10 Mar 2020"},{"key":"11533_CR18","doi-asserted-by":"publisher","unstructured":"Dong F, Wang H, Li L, Guo Y, Bissyand\u00e9 TF, Liu T, Xu G, Klein J (2018) Frauddroid: automated ad fraud detection for android apps. In: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. pp 257\u2013268. https:\/\/doi.org\/10.1145\/3236024.3236045","DOI":"10.1145\/3236024.3236045"},{"key":"11533_CR19","unstructured":"Drummond N, Horridge M, Knublauch H (2005) Prot\u00e9g\u00e9-OWL tutorial. In: 8th International Prot\u00e9g\u00e9 Conference"},{"key":"11533_CR20","doi-asserted-by":"publisher","unstructured":"El Orche A, Bahaj M (2019) Approach to use ontology based on electronic payment system and machine learning to prevent Fraud. In: Proceedings of the 2nd International Conference on Networking, Information Systems & Security.\u00a0Rabat, Morocco, pp 1\u20136. https:\/\/doi.org\/10.1145\/3320326.3320369","DOI":"10.1145\/3320326.3320369"},{"key":"11533_CR21","doi-asserted-by":"publisher","unstructured":"El-Atawy SS, Khalefa ME (2016) Building an ontology-based electronic health record system. In: Proceedings of the 2nd Africa and Middle East Conference on Software Engineering.\u00a0pp 40\u201345. https:\/\/doi.org\/10.1145\/2944165.2944172","DOI":"10.1145\/2944165.2944172"},{"key":"11533_CR22","doi-asserted-by":"publisher","unstructured":"Fang L, Cai M, Fu H, Dong J (2007) Ontology-based fraud detection. In: International Conference on Computational Science. Springer, Berlin, Heidelberg, pp 1048\u20131055. https:\/\/doi.org\/10.1007\/978-3-540-72588-6_168","DOI":"10.1007\/978-3-540-72588-6_168"},{"key":"11533_CR23","doi-asserted-by":"publisher","unstructured":"Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: European conference on computational learning theory. Springer, Berlin, Heidelberg, pp 23\u201337. https:\/\/doi.org\/10.1007\/3-540-59119-2_166","DOI":"10.1007\/3-540-59119-2_166"},{"issue":"5","key":"11533_CR24","first-page":"771","volume":"14","author":"Y Freund","year":"1999","unstructured":"Freund Y, Schapire R, Abe N (1999) A short introduction to boosting. J Jpn Soc Artif Intell 14(5):771\u2013780","journal-title":"J Jpn Soc Artif Intell"},{"key":"11533_CR25","doi-asserted-by":"publisher","unstructured":"Gabryel M (2018) Data analysis algorithm for click fraud recognition. In: International Conference on Information and Software Technologies. Springer, Cham, pp 437\u2013446. https:\/\/doi.org\/10.1007\/978-3-319-99972-2_36","DOI":"10.1007\/978-3-319-99972-2_36"},{"key":"11533_CR26","doi-asserted-by":"publisher","unstructured":"Gabryel M, Przybyszewski K (2019) The dynamically modified BoW algorithm used in assessing clicks in online ads. In: International Conference on Artificial Intelligence and Soft Computing. Springer, Cham, pp 350\u2013360. https:\/\/doi.org\/10.1007\/978-3-030-20915-5_32","DOI":"10.1007\/978-3-030-20915-5_32"},{"key":"11533_CR27","first-page":"1","volume-title":"Handbook on ontologies","author":"N Guarino","year":"2009","unstructured":"Guarino N, Oberle D, Staab S (2009) What is an ontology? Handbook on ontologies. Springer, Berlin, Heidelberg, pp 1\u201317"},{"key":"11533_CR28","unstructured":"Gupta N, Le HA, Boldina M, Woo J (2019) Predicting fraud of AD click using Traditional and Spark ML. In: The 14th Asia Pacific International Conference on Information Science and Technology (APIC-IST). Beijing, China, pp 24\u201328"},{"key":"11533_CR29","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.jnca.2018.02.021","volume":"112","author":"CMR Haider","year":"2018","unstructured":"Haider CMR, Iqbal A, Rahman AH, Rahman MS (2018) An ensemble learning based approach for impression fraud detection in mobile advertising. J Netw Comput Appl 112:126\u2013141. https:\/\/doi.org\/10.1016\/j.jnca.2018.02.021","journal-title":"J Netw Comput Appl"},{"issue":"3","key":"11533_CR30","doi-asserted-by":"publisher","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","volume":"2","author":"T Hastie","year":"2009","unstructured":"Hastie T, Rosset S, Zhu J, Zou H (2009) Multi-class adaboost. Stat Interface 2(3):349\u2013360. https:\/\/doi.org\/10.4310\/SII.2009.v2.n3.a8","journal-title":"Stat Interface"},{"issue":"5","key":"11533_CR31","first-page":"1","volume":"1","author":"H Hlomani","year":"2014","unstructured":"Hlomani H, Stacey D (2014) Approaches, methods, metrics, measures, and subjectivity in ontology evaluation: a survey. Semant Web J 1(5):1\u201311","journal-title":"Semant Web J"},{"key":"11533_CR32","unstructured":"Imperva Incapsula. Bot Traffic Report 2016. (2017) [online] Available: https:\/\/www.incapsula.com\/blog\/bot-traffic-report-2016.html, Accessed from 20 May 2020"},{"key":"11533_CR33","doi-asserted-by":"publisher","unstructured":"Iqbal MS, Zulkernine M, Jaafar F, Gu Y (2016) Fcfraud: fighting click-fraud from the user side. In: 2016 IEEE 17th International Symposium on High Assurance Systems Engineering (HASE). IEEE. pp 157\u2013164. https:\/\/doi.org\/10.1109\/HASE.2016.17","DOI":"10.1109\/HASE.2016.17"},{"issue":"6","key":"11533_CR34","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ecoinf.2010.06.003","volume":"5","author":"C Kampichler","year":"2010","unstructured":"Kampichler C, Wieland R, Calm\u00e9 S, Weissenberger H, Arriaga-Weiss S (2010) Classification in conservation biology: a comparison of five machine-learning methods. Eco Inform 5(6):441\u2013450. https:\/\/doi.org\/10.1016\/j.ecoinf.2010.06.003","journal-title":"Eco Inform"},{"key":"11533_CR35","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.jnca.2018.03.015","volume":"112","author":"R Kaur","year":"2018","unstructured":"Kaur R, Singh S, Kumar H (2018) Rise of spam and compromised accounts in online social networks: a state-of-the-art review of different combating approaches. J Netw Comput Appl 112:53\u201388. https:\/\/doi.org\/10.1016\/j.jnca.2018.03.015","journal-title":"J Netw Comput Appl"},{"key":"11533_CR36","doi-asserted-by":"crossref","unstructured":"Kerremans K, Tang Y, Temmerman R, Zhao G (2005) Towards ontology-based e-mail fraud detection. In: 2005 portuguese conference on artificial intelligence. IEEE, pp 106\u2013111","DOI":"10.1109\/EPIA.2005.341275"},{"key":"11533_CR37","first-page":"187","volume-title":"Data privacy management and autonomous spontaneous security","author":"N Kheir","year":"2012","unstructured":"Kheir N (2012) Analyzing http user agent anomalies for malware detection. Data privacy management and autonomous spontaneous security. Springer, Berlin, Heidelberg, pp 187\u2013200"},{"key":"11533_CR38","doi-asserted-by":"publisher","unstructured":"La VH, Fuentes R, Cavalli AR (2016) Network monitoring using mmt: an application based on the user-agent field in http headers. In: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA). IEEE, pp 147\u2013154. https:\/\/doi.org\/10.1109\/AINA.2016.41","DOI":"10.1109\/AINA.2016.41"},{"key":"11533_CR39","doi-asserted-by":"publisher","DOI":"10.5171\/2019.263928","author":"EA Minastireanu","year":"2019","unstructured":"Minastireanu EA, Mesnita G (2019) Light gbm machine learning algorithm to online click fraud detection.\u00a0J Inf Assur Cybersecur. https:\/\/doi.org\/10.5171\/2019.263928.","journal-title":"J Int Assur Cybersecur"},{"key":"11533_CR40","doi-asserted-by":"publisher","unstructured":"Mladenow A, Novak NM, Strauss C (2015) Online ad-fraud in search engine advertising campaigns. In: Information and Communication Technology-EurAsia Conference. Springer, Cham, pp 109\u2013118. https:\/\/doi.org\/10.1007\/978-3-319-24315-3_11","DOI":"10.1007\/978-3-319-24315-3_11"},{"key":"11533_CR41","doi-asserted-by":"publisher","unstructured":"Mungamuru B, Weis S (2008).Competition and fraud in online advertising markets. In: International Conference on Financial Cryptography and Data Security. Springer, Berlin, Heidelberg, pp 187\u2013191. https:\/\/doi.org\/10.1007\/978-3-540-85230-8_16","DOI":"10.1007\/978-3-540-85230-8_16"},{"key":"11533_CR42","doi-asserted-by":"publisher","unstructured":"Nagaraja S, Shah R (2019) Clicktok: click fraud detection using traffic analysis. In: Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks. pp 105\u2013116. https:\/\/doi.org\/10.1145\/3317549.3323407","DOI":"10.1145\/3317549.3323407"},{"key":"11533_CR43","doi-asserted-by":"publisher","unstructured":"Obeid M, Obeid Z, Moubaiddin A, Obeid N (2019) Using description logic and abox abduction to capture medical diagnosis. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, Cham, pp 376\u2013388. https:\/\/doi.org\/10.1007\/978-3-030-22999-3_33","DOI":"10.1007\/978-3-030-22999-3_33"},{"key":"11533_CR44","unstructured":"Papadopoulos P, Azurmendi IQ, Zhang J, Varvello M, Nappa A, Livshits B (2019) ZKSENSE: a privacy-preserving mechanism for bot detection in mobile devices. arXiv preprint arXiv:1911.07649"},{"issue":"1","key":"11533_CR45","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF00116251","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81\u2013106. https:\/\/doi.org\/10.1007\/BF00116251","journal-title":"Mach Learn"},{"issue":"5","key":"11533_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijsptm.2012.1501","volume":"1","author":"AA Ramaki","year":"2012","unstructured":"Ramaki AA, Asgari R, Atani RE (2012) Credit card fraud detection based on ontology graph. International Journal of Security, Privacy and Trust Management (IJSPTM) 1(5):1\u201312","journal-title":"International Journal of Security, Privacy and Trust Management (IJSPTM)"},{"key":"11533_CR47","unstructured":"Segal MR (2004) Machine learning benchmarks and random forest regression. UCSF: Center for Bioinformatics and Molecular Biostatistics [online], Available: https:\/\/escholarship.org\/uc\/item\/35x3v9t4, Accessed from 20 May 2020"},{"key":"11533_CR48","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.diin.2018.12.005","volume":"28","author":"M Singh","year":"2019","unstructured":"Singh M, Singh M, Kaur S (2019) Detecting bot-infected machines using DNS fingerprinting. Digit Investig 28:14\u201333","journal-title":"Digit Investig"},{"key":"11533_CR49","first-page":"165","volume-title":"The top ten algorithms in data mining","author":"M Steinbach","year":"2009","unstructured":"Steinbach M, Tan PN (2009) kNN: k-nearest neighbors. The top ten algorithms in data mining. Chapman and Hall\/CRC, Florida, pp 165\u2013176"},{"key":"11533_CR50","unstructured":"Stenberg D (2018) Everything cUR. [Online]. Available: https:\/\/ec.haxx.se\/, Accessed from 20 May 2020"},{"issue":"3","key":"11533_CR51","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293\u2013300. https:\/\/doi.org\/10.1023\/A:1018628609742","journal-title":"Neural Process Lett"},{"issue":"3","key":"11533_CR52","doi-asserted-by":"publisher","first-page":"205","DOI":"10.5771\/0943-7444-2018-3-205","volume":"45","author":"XB Tang","year":"2018","unstructured":"Tang XB, Liu GC, Yang J, Wei W (2018) Knowledge-based financial statement fraud detection system: based on an ontology and a decision tree. KO Knowl Organ 45(3):205\u2013219","journal-title":"KO Knowl Organ"},{"key":"11533_CR53","unstructured":"The prot\u00e9g\u00e9 development package. https:\/\/protege.stanford.edu\/, Visisted from 20 May 2020"},{"key":"11533_CR54","doi-asserted-by":"publisher","unstructured":"Thejas GS, Boroojeni KG, Chandna K, Bhatia I, Iyengar SS, Sunitha NR (2019) Deep learning-based model to fight against ad click fraud. In: Proceedings of the 2019 ACM Southeast Conference. pp 176\u2013181. https:\/\/doi.org\/10.1145\/3299815.3314453","DOI":"10.1145\/3299815.3314453"},{"key":"11533_CR55","doi-asserted-by":"publisher","unstructured":"Wang AH (2010) Detecting spam bots in online social networking sites: a machine learning approach. In: IFIP Annual Conference on Data and Applications Security and Privac. Springer, Berlin, Heidelberg, pp 335\u2013342. https:\/\/doi.org\/10.1007\/978-3-642-13739-6_25","DOI":"10.1007\/978-3-642-13739-6_25"},{"key":"11533_CR56","unstructured":"Waseet Classified Ads. (2019) [online]. Available: http:\/\/waseet.net, Accessed from 20 May 2020"},{"issue":"7","key":"11533_CR57","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1162\/neco.1996.8.7.1341","volume":"8","author":"DH Wolpert","year":"1996","unstructured":"Wolpert DH (1996) The lack of a priori distinctions between learning algorithms. Neural Comput 8(7):1341\u20131390","journal-title":"Neural Comput"},{"key":"11533_CR58","doi-asserted-by":"publisher","unstructured":"Zarras A, Kapravelos A, Stringhini G, Holz T, Kruegel C, Vigna G (2014) The dark alleys of Madison Avenue: Understanding malicious advertisements. In: Proceedings of the 2014 Conference on Internet Measurement Conference.\u00a0pp 373\u2013380. https:\/\/doi.org\/10.1145\/2663716.2663719","DOI":"10.1145\/2663716.2663719"},{"key":"11533_CR59","unstructured":"Zhang M, Meng W, Lee S, Lee B, Xing X (2019) All your clicks belong to me: investigating click interception on the web. In: Proceedings of 28th USENIX Security Symposium. Santa Clara, CA, USA, pp 941\u2013957"},{"issue":"2","key":"11533_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.03.004","volume":"57","author":"X Zhang","year":"2020","unstructured":"Zhang X, Ghorbani AA (2020) An overview of online fake news: characterization, detection, and discussion. Inf Process Manage 57(2):102025. https:\/\/doi.org\/10.1016\/j.ipm.2019.03.004","journal-title":"Inf Process Manage"},{"key":"11533_CR61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-56793-8","volume-title":"Fraud prevention in online digital advertising","author":"X Zhu","year":"2017","unstructured":"Zhu X, Tao H, Wu Z, Cao J, Kalish K, Kayne J (2017) Fraud prevention in online digital advertising. Springer International Publishing, NewYork"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11533-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-11533-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-11533-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T09:20:17Z","timestamp":1643448017000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-11533-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,11]]},"references-count":61,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["11533"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-11533-4","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,10,11]]},"assertion":[{"value":"25 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}