{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:53:31Z","timestamp":1754157211533,"version":"3.41.2"},"reference-count":29,"publisher":"Emerald","issue":"7","license":[{"start":{"date-parts":[[2004,9,1]],"date-time":"2004-09-01T00:00:00Z","timestamp":1093996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2004,9,1]]},"abstract":"<jats:p>New malicious e\u2010mails are created at the rate of thousands a year and pose a serious security threat. Especially, new, unseen Internet worms and virus often are arriving as e\u2010mail attachments. In this paper, Bayesian probabilistic network is examined to detect new malicious e\u2010mail viruses through anomaly detection. Experimental results show a better malicious e\u2010mail detection using Bayesian probabilistic networks. Managerial implications on how companies can protect their e\u2010mails and develop their own e\u2010mail security plan are addressed as well.<\/jats:p>","DOI":"10.1108\/02635570410550278","type":"journal-article","created":{"date-parts":[[2004,9,29]],"date-time":"2004-09-29T15:20:53Z","timestamp":1096471253000},"page":"613-623","source":"Crossref","is-referenced-by-count":27,"title":["Internet security: malicious e\u2010mails detection and protection"],"prefix":"10.1108","volume":"104","author":[{"given":"Shih","family":"Dong\u2010Her","sequence":"first","affiliation":[]},{"given":"Chiang","family":"Hsiu\u2010Sen","sequence":"additional","affiliation":[]},{"given":"Chan","family":"Chun\u2010Yuan","sequence":"additional","affiliation":[]},{"given":"Binshan","family":"Lin","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022021019450963200_b1","unstructured":"Arnold, W. and Tesauro, G. 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