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Detecting and analyzing phishing attacks through artificial intelligence

專利號(hào)
US11997138B1
公開日期
2024-05-28
申請(qǐng)人
KING FAISAL UNIVERSITY(SA Al Hasa)
發(fā)明人
Ahmed Alyahya; Mohammed Alzahrani
IPC分類
H04L9/40
技術(shù)領(lǐng)域
phishing,email,persuasion,message,spam,emails,in,recipient,signs,or
地域: Al Hasa

摘要

Detection of phishing messages in network communications is performed by receiving a transmitted message and detecting characteristics of the message. A determination is made if the message matches a pattern of a phishing message in a database, and classifies the message as a phishing or spam message accordingly. If the message does not match a known phishing message pattern, the message is checked for common signs of phishing or spam by determining the severity of a threat embodied by the message, and the message is categorized as having phishing characteristics and according to the severity of threat. In response the user responses to determinations of threats, criteria for detection of phishing characteristics is adjusted, thereby automatically revising criteria for future decisions as to whether the message represents suspected phishing.

說明書

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FIG. 1 is a flow diagram showing the disclosed sequence. The sequence is initiated on receiving a transmitted message and detecting characteristics of the message. In the example, the messages are emails; however other forms of electronic communication, including SMS messages, “PM” messages, and other forms of electronic messages are treated similarly. The characteristics of the email can be the message text, the message text with formatting, images, image conversions, video sequences and other forms of network-transmitted communication. Also included are text equivalents such as a Cyrillic character that shares the general appearance of a Latin character in Latin text (or vice-versa). Some of these characteristics can, by themselves, be spam indicators, such as mixing look-alike Latin and Cyrillic characters. Other characteristics are part of the normal intended text, such as an Arabic or Cyrillic word or expression in a non-Cyrillic sentence.

Other characteristics would include URLs, including look-alike URLs, images, images representing text and other information transmitted in a message.

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