{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:43:16Z","timestamp":1768524196808,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683164","type":"print"},{"value":"9781643683171","type":"electronic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,14]]},"abstract":"<jats:p>Spam consists of unwanted messages that are often containers of malicious code and\/or links pointing to shady sites or objects that pose real dangers to a company\u2019s machines, software, or data. Spam detection is therefore a primary security objective. Nevertheless, the detection tools available on the market are few in number and their efficiency is often limited. In this paper, we propose a spam detection tool based on deep-learning. Our tool uses bidirectional Long-Short Term Memory networks while relying on Stanford Global Vectors for word representation. We present the techniques we use. Then, we conduct a series of experiments on a family of candidate detectors. Finally, we present the performance of the selected detector.<\/jats:p>","DOI":"10.3233\/faia220246","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:07:42Z","timestamp":1663319262000},"source":"Crossref","is-referenced-by-count":10,"title":["SpamDL: A High Performance Deep Learning Spam Detector Using Stanford Global Vectors and Bidirectional Long Short-Term Memory Neural Networks"],"prefix":"10.3233","author":[{"given":"Jaouhar","family":"Fattahi","sequence":"first","affiliation":[{"name":"D\u00e9partement d\u2019informatique et de g\u00e9nie logiciel, Universit\u00e9 Laval, 2325, rue de l\u2019Universit\u00e9, Qu\u00e9bec (Qu\u00e9bec) G1V 0A6, Canada"},{"name":"Minist\u00e8re de la Cybers\u00e9curit\u00e9 et du Num\u00e9rique, DGSSI (DAS), 1500, rue Cyrille-Duquet, Qu\u00e9bec (Qu\u00e9bec) G1N 4T6, Canada"}]},{"given":"Mohamed","family":"Mejri","sequence":"additional","affiliation":[{"name":"D\u00e9partement d\u2019informatique et de g\u00e9nie logiciel, Universit\u00e9 Laval, 2325, rue de l\u2019Universit\u00e9, Qu\u00e9bec (Qu\u00e9bec) G1V 0A6, Canada"}]},{"given":"Marwa","family":"Ziadia","sequence":"additional","affiliation":[{"name":"D\u00e9partement d\u2019informatique et de g\u00e9nie logiciel, Universit\u00e9 Laval, 2325, rue de l\u2019Universit\u00e9, Qu\u00e9bec (Qu\u00e9bec) G1V 0A6, Canada"}]},{"given":"Ridha","family":"Ghayoula","sequence":"additional","affiliation":[{"name":"Minist\u00e8re de la Cybers\u00e9curit\u00e9 et du Num\u00e9rique, DGSSI (DAS), 1500, rue Cyrille-Duquet, Qu\u00e9bec (Qu\u00e9bec) G1N 4T6, Canada"},{"name":"D\u00e9partement de g\u00e9nie \u00e9lectrique et g\u00e9nie informatique, Universit\u00e9 Laval, 2325, rue de l\u2019Universit\u00e9, Qu\u00e9bec (Qu\u00e9bec) G1V 0A6, Canada"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220246","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:07:52Z","timestamp":1663319272000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220246"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"ISBN":["9781643683164","9781643683171"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220246","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,14]]}}}