{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T13:08:25Z","timestamp":1774357705524,"version":"3.50.1"},"reference-count":0,"publisher":"Inderscience Publishers","issue":"2\/3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJBIDM"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1504\/ijbidm.2026.152484","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:30:30Z","timestamp":1774355430000},"page":"230-242","source":"Crossref","is-referenced-by-count":0,"title":["Attention and deep feature-based intelligent approach for abnormal network traffic detection"],"prefix":"10.1504","volume":"28","author":[{"given":"Guihua","family":"Wu","sequence":"first","affiliation":[]}],"member":"378","container-title":["International Journal of Business Intelligence and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.inderscienceonline.com\/doi\/full\/10.1504\/IJBIDM.2026.152484","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:30:32Z","timestamp":1774355432000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.inderscience.com\/link.php?id=152484"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"journal-issue":{"issue":"2\/3","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.1504\/ijbidm.2026.152484","relation":{},"ISSN":["1743-8187","1743-8195"],"issn-type":[{"value":"1743-8187","type":"print"},{"value":"1743-8195","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"article-number":"152484"}}