{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T22:54:36Z","timestamp":1775516076498,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,3,3]],"date-time":"2017-03-03T00:00:00Z","timestamp":1488499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The critical process of hiring has relatively recently been ported to the cloud. Specifically, the automated systems responsible for completing the recruitment of new employees in an online fashion, aim to make the hiring process more immediate, accurate and cost-efficient. However, the online exposure of such traditional business procedures has introduced new points of failure that may lead to privacy loss for applicants and harm the reputation of organizations. So far, the most common case of Online Recruitment Frauds (ORF), is employment scam. Unlike relevant online fraud problems, the tackling of ORF has not yet received the proper attention, remaining largely unexplored until now. Responding to this need, the work at hand defines and describes the characteristics of this severe and timely novel cyber security research topic. At the same time, it contributes and evaluates the first to our knowledge publicly available dataset of 17,880 annotated job ads, retrieved from the use of a real-life system.<\/jats:p>","DOI":"10.3390\/fi9010006","type":"journal-article","created":{"date-parts":[[2017,3,3]],"date-time":"2017-03-03T11:30:04Z","timestamp":1488540604000},"page":"6","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset"],"prefix":"10.3390","volume":"9","author":[{"given":"Sokratis","family":"Vidros","sequence":"first","affiliation":[{"name":"Department of Information &amp; Communication Systems Engineering, University of the Aegean, Karlovassi, Samos 83200, Greece"}]},{"given":"Constantinos","family":"Kolias","sequence":"additional","affiliation":[{"name":"Computer Science Department, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6348-5031","authenticated-orcid":false,"given":"Georgios","family":"Kambourakis","sequence":"additional","affiliation":[{"name":"Department of Information &amp; Communication Systems Engineering, University of the Aegean, Karlovassi, Samos 83200, Greece"},{"name":"Computer Science Department, George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Leman","family":"Akoglu","sequence":"additional","affiliation":[{"name":"H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA 15213, USA"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s10462-009-9109-6","article-title":"A survey of learning-based techniques of email spam filtering","volume":"29","author":"Blanzieri","year":"2008","journal-title":"Artif. Intell. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10206","DOI":"10.1016\/j.eswa.2009.02.037","article-title":"A review of machine learning approaches to spam filtering","volume":"36","author":"Guzella","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"153","DOI":"10.4236\/cn.2011.33019","article-title":"Survey on spam filtering techniques","volume":"3","author":"Saadat","year":"2011","journal-title":"Commun. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Abu-Nimeh, S., Nappa, D., Wang, X., and Nair, S. (2007, January 4\u20135). A comparison of machine learning techniques for phishing detection. Proceedings of the Anti-Phishing Working Groups 2nd Annual eCrime Researchers Summit, Pittsburgh, PA, USA.","DOI":"10.1145\/1299015.1299021"},{"key":"ref_5","unstructured":"Potthast, M., Stein, B., and Gerling, R. (2008). Advances in Information Retrieval, Springer."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Potthast, M. (2010, January 19\u201323). Crowdsourcing a wikipedia vandalism corpus. Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Geneva, Switzerland.","DOI":"10.1145\/1835449.1835617"},{"key":"ref_7","unstructured":"Dinakar, K., Reichart, R., and Lieberman, H. (2011, January 17\u201321). Modeling the detection of Textual Cyberbullying. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhou, Y., Zhu, S., and Xu, H. (2012, January 3\u20135). Detecting offensive language in social media to protect adolescent online safety. Proceedings of the 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing, Amsterdam, The Netherlands.","DOI":"10.1109\/SocialCom-PASSAT.2012.55"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Potha, N., and Maragoudakis, M. (2013, January 7\u201310). Cyberbullying Detection using Time Series Modeling. Proceedings of the 2014 IEEE International Conference on Data Mining Workshop (ICDMW), Dallas, TX, USA.","DOI":"10.1109\/ICDMW.2014.170"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3634","DOI":"10.1016\/j.eswa.2014.12.029","article-title":"Detection of review spam: A survey","volume":"42","author":"Heydari","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_11","unstructured":"Laboratory of Information and Communication Systems, University of the Aegean, Samos, Greece EMSCAD Employment Scam Aegean Dataset. Available online: http:\/\/icsdweb.aegean.gr\/emscad."},{"key":"ref_12","unstructured":"Peggs, M. Applicant Tracking Systems Solved. Available online: http:\/\/www.michaelpeggs.com\/applicant-tracking-systems-solved."},{"key":"ref_13","unstructured":"Bloomberg There Are Now More Than Five Million Job Openings in America. Available online: http:\/\/www.bloomberg.com\/news\/articles\/2015-02-10\/job-openings-in-u-s-rose-by-181-000-in-december-to-5-03-million."},{"key":"ref_14","unstructured":"Jobvite Social recruiting survey for 2014. Available online: http:\/\/web.jobvite.com\/rs\/jobvite\/images\/2014%20Job%20Seeker%20Survey.pdf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/S1361-3723(16)30025-2","article-title":"Online recruitment services: Another playground for fraudsters","volume":"2016","author":"Vidros","year":"2016","journal-title":"Comput. Fraud Secur."},{"key":"ref_16","unstructured":"Auld, E. Man Posts Fake Job on Craigslist, Gets 600+ Resumes. Available online: http:\/\/chemjobber.blogspot.gr\/2012\/08\/man-posts-fake-job-on-craigslist-gets.html."},{"key":"ref_17","unstructured":"Australian Bureau of Statistics (2012). Personal Fraud, Available online: http:\/\/www.abs.gov.au\/AUSSTATS\/abs@.nsf\/mf\/4528.0."},{"key":"ref_18","unstructured":"Workable. Available online: https:\/\/www.workable.com."},{"key":"ref_19","unstructured":"CareerBuilder Think You Can Spot a Fake Resume?. Available online: http:\/\/thehiringsite.careerbuilder.com\/2012\/05\/04\/think-you-can-spot-a-fake-resume."},{"key":"ref_20","unstructured":"Indeed Job Forum. Available online: http:\/\/www.indeed.com\/forum."},{"key":"ref_21","unstructured":"Mashable 10 Signes a Job Is a Scam. Available online: http:\/\/mashable.com\/2013\/10\/05\/10-signs-a-job-is-a-scam."},{"key":"ref_22","unstructured":"Monster.com Money-Laundering and Reshipping Scams. Available online: http:\/\/inside.monster.com\/money-laundering\/inside2.aspx."},{"key":"ref_23","unstructured":"Malwarebytes Money Mules, If It Looks Too Good to Be True.... Available online: https:\/\/blog.malwarebytes.com\/threat-analysis\/2013\/10\/money-mules-if-it-looks-too-good-to-be-true\/."},{"key":"ref_24","unstructured":"Straus, R.R. Fake Job Offer Scam Dupes Thousands into Laundering Money for Criminal Gangs. Available online: http:\/\/www.thisismoney.co.uk\/money\/news\/article-2284737\/Fake-job-offer-scam-dupes-thousands-laundering-money-criminal-gangs.html."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., and Spyropoulos, C.D. (2000, January 24\u201328). An Experimental Comparison of Naive Bayesian and Keyword-based Anti-spam Filtering with Personal e-Mail Messages. Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Athens, Greece.","DOI":"10.1145\/345508.345569"},{"key":"ref_26","unstructured":"Agrawal, B., Kumar, N., and Molle, M. (2005, January 15\u201319). Controlling spam emails at the routers. Proceedings of the ICC 2005\u20142005 IEEE International Conference on Communications, Seoul, Korea."},{"key":"ref_27","unstructured":"SPF Council Sender Policy Framework. Available online: http:\/\/www.openspf.org."},{"key":"ref_28","unstructured":"Schiavone, V., Brussin, D., Koenig, J., Cobb, S., and Everett-Church, R. (2003). Trusted Email Open Standard, Copyright ePrivacy Group. White Paper, May 2003."},{"key":"ref_29","unstructured":"Microsoft corporation Sender ID. Available online: http:\/\/www.microsoft.com\/mscorp\/safety\/technologies\/senderid\/default.mspx."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kuipers, B.J., Liu, A.X., Gautam, A., and Gouda, M.G. (2005, January 6\u201310). Zmail: Zero-sum free market control of spam. Proceedings of the 25th IEEE International Conference on Distributed Computing Systems Workshops, Columbus, OH, USA.","DOI":"10.1109\/ICDCSW.2005.144"},{"key":"ref_31","unstructured":"Leiba, B., Ossher, J., Rajan, V., Segal, R., and Wegman, M.N. (2005, January 21\u201322). SMTP Path Analysis. Proceedings of the CEAS 2005 Second Conference on Email and Anti-Spam, Stanford, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MC.2005.132","article-title":"Leveraging social networks to fight spam","volume":"38","author":"Oscar","year":"2005","journal-title":"IEEE Comput."},{"key":"ref_33","unstructured":"Pantel, P., and Lin, D. (1998, January 26\u201327). Spamcop: A spam classification & organization program. Proceedings of the AAAI-98 Workshop on Learning for Text Categorization, Madison, WI, USA."},{"key":"ref_34","unstructured":"Graham, P. A Plan for Spam. Available online: http:\/\/www.paulgraham.com\/spam.html."},{"key":"ref_35","unstructured":"Androutsopoulos, I., Paliouras, G., and Michelakis, E. (2004). Learning to Filter Unsolicited Commercial E-mail, National Center for Scientific Research \u201cDEMOKRITOS\u201d."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1142\/S0218213007003692","article-title":"Words versus character n-grams for anti-spam filtering","volume":"16","author":"Kanaris","year":"2007","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.patrec.2007.07.018","article-title":"Time-efficient spam e-mail filtering using n-gram models","volume":"29","year":"2008","journal-title":"Pattern Recogn. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1109\/72.788645","article-title":"Support vector machines for spam categorization","volume":"10","author":"Drucker","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sculley, D., and Wachman, G.M. (2007, January 23\u201327). Relaxed online SVMs for spam filtering. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, The Netherlands.","DOI":"10.1145\/1277741.1277813"},{"key":"ref_40","unstructured":"Yeh, C.Y., Wu, C.H., and Doong, S.H. (2005, January 10\u201312). Effective spam classification based on meta-heuristics. Proceedings of the 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, USA."},{"key":"ref_41","unstructured":"Hershkop, S. (2006). Behavior-Based Email Analysis with Application to Spam Detection. [Ph.D. Thesis, Columbia University]."},{"key":"ref_42","first-page":"2673","article-title":"Spam filtering using statistical data compression models","volume":"7","author":"Bratko","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","unstructured":"Akhawe, D., and Felt, A.P. (2013, January 14\u201316). Alice in Warningland: A Large-Scale Field Study of Browser Security Warning Effectiveness. Proceedings of the 22nd USENIX conference on Security, Washington, DC, USA."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hong, J.I., and Cranor, L.F. (2007, January 8\u201312). Cantina: A content-based approach to detecting phishing web sites. Proceedings of the 16th international conference on World Wide Web, Banff, AB, Canada.","DOI":"10.1145\/1242572.1242659"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wenyin, L., Huang, G., Xiaoyue, L., Min, Z., and Deng, X. (2005, January 10\u201314). Detection of phishing webpages based on visual similarity. Proceedings of the Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, Chiba, Japan.","DOI":"10.1145\/1062745.1062868"},{"key":"ref_46","unstructured":"Wang, W.Y., and McKeown, K.R. (2010, January 23\u201327). Got you!: Automatic vandalism detection in Wikipedia with web-based shallow syntactic-semantic modeling. Proceedings of the 23rd International Conference on Computational Linguistics, Beijing, China."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chin, S.C., Street, W.N., Srinivasan, P., and Eichmann, D. (2010, January 26\u201330). Detecting Wikipedia vandalism with active learning and statistical language models. Proceedings of the 4th Workshop on Information Credibility, Raleigh, NC, USA.","DOI":"10.1145\/1772938.1772942"},{"key":"ref_48","unstructured":"Harpalani, M., Hart, M., Singh, S., Johnson, R., and Choi, Y. (2011, January 19\u201324). Language of vandalism: Improving Wikipedia vandalism detection via stylometric analysis. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-Volume 2, Portland, Oregon."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"West, A.G., Kannan, S., and Lee, I. (2010, January 13\u201316). Detecting Wikipedia vandalism via spatio-temporal analysis of revision metadata?. Proceedings of the Third European Workshop on System Security, Paris, France.","DOI":"10.1145\/1752046.1752050"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dadvar, M., and De Jong, F. (2012, January 16\u201320). Cyberbullying detection: A step toward a safer Internet yard. Proceedings of the 21st International Conference Companion on World Wide Web, Lyon, France.","DOI":"10.1145\/2187980.2187995"},{"key":"ref_51","unstructured":"Dadvar, M., de Jong, F., Ordelman, R., and Trieschnigg, R. (2012, January 23\u201324). Improved cyberbullying detection using gender information. Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop, DIR 2012, Ghent, Belgium."},{"key":"ref_52","unstructured":"Dadvar, M., Trieschnigg, D., Ordelman, R., and de Jong, F. (2013). Advances in Information Retrieval, Springer."},{"key":"ref_53","unstructured":"Cheng, J., Danescu-Niculescu-Mizil, C., and Leskovec, J. (arXiv, 2015). Antisocial Behavior in Online Discussion Communities, arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"13417","DOI":"10.1016\/j.eswa.2012.05.061","article-title":"Automatic categorisation of comments in social news websites","volume":"39","author":"Santos","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, Z., Liu, B., Wei, X., and Shao, J. (2014, January 14\u201317). Spotting fake reviews via collective positive-unlabeled learning. Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM), Shenzhen, China.","DOI":"10.1109\/ICDM.2014.47"},{"key":"ref_56","unstructured":"Mukherjee, A., Venkataraman, V., Liu, B., and Glance, N.S. (2013, January 8\u201311). What yelp fake review filter might be doing?. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, Cambridge, MA, USA."},{"key":"ref_57","unstructured":"Ott, M., Choi, Y., Cardie, C., and Hancock, J.T. (2011, January 19\u201324). Finding deceptive opinion spam by any stretch of the imagination. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, Portland, Oregon."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ott, M., Cardie, C., and Hancock, J. (2012, January 16\u201320). Estimating the prevalence of deception in online review communities. Proceedings of the 21st international conference on World Wide Web, Lyon, France.","DOI":"10.1145\/2187836.2187864"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Banerjee, S., and Chua, A.Y. (2014, January 27\u201329). Applauses in hotel reviews: Genuine or deceptive?. Proceedings of the Science and Information Conference (SAI), London, UK.","DOI":"10.1109\/SAI.2014.6918299"},{"key":"ref_60","unstructured":"Feng, S., Banerjee, R., and Choi, Y. (2012, January 8\u201314). Syntactic stylometry for deception detection. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2, Jeju Island, Korea."},{"key":"ref_61","unstructured":"Li, H., Chen, Z., Mukherjee, A., Liu, B., and Shao, J. (2015, January 26\u201329). Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns. Proceedings of the Ninth International AAAI Conference on Web and Social Media, Oxford, UK."},{"key":"ref_62","first-page":"175","article-title":"Exploiting Burstiness in Reviews for Review Spammer Detection","volume":"13","author":"Fei","year":"2013","journal-title":"ICWSM"},{"key":"ref_63","first-page":"2","article-title":"Opinion Fraud Detection in Online Reviews by Network Effects","volume":"13","author":"Akoglu","year":"2013","journal-title":"ICWSM"},{"key":"ref_64","unstructured":"Apache Lucene. Available online: http:\/\/lucene.apache.org\/core\/."},{"key":"ref_65","unstructured":"The university of Waikato Weka. Available online: http:\/\/www.cs.waikato.ac.nz\/ml\/weka."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/9\/1\/6\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:29:36Z","timestamp":1760207376000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/9\/1\/6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,3]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,3]]}},"alternative-id":["fi9010006"],"URL":"https:\/\/doi.org\/10.3390\/fi9010006","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,3]]}}}