Visual deep learning for inline phishing detection

A method, comprising:

• extracting a logo from a screenshot of a web page;
• detecting phishing based on a match to at least one of a plurality of reference logos using a visual deep learning model and that a domain associated with the web page is not associated with an entity that matches the logo extracted from the web page, comprising:
• inputting the extracted logo into the visual deep learning model to output a first feature vector and a second feature vector, wherein the visual deep learning model passes the extracted logo to at least two different sub-networks to generate the first feature vector and the second feature vector;
• inputting a combination of the first feature vector and the second feature vector into a neural network to generate a logo embedding;
• determining a similarity of the logo embedding associated with the logo and a reference logo embedding associated with the at least one reference logo;
• and
• in response to a determination that the similarity fails to equal or exceed a similarity threshold, determining that the web page is associated with phishing;
• and
• performing a remedial action in response to determining that the web page is associated with phishing.

A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for:

• extracting a logo from a screenshot of a web page;
• detecting phishing based on a match to at least one of a plurality of reference logos using a visual deep learning model and that a domain associated with the web page is not associated with an entity that matches the logo extracted from the web page, comprising:
• inputting the extracted logo into the visual deep learning model to output a first feature vector and a second feature vector, wherein the visual deep learning model passes the extracted logo to at least two different sub-networks to generate the first feature vector and the second feature vector;
• inputting a combination of the first feature vector and the second feature vector into a neural network to generate a logo embedding;
• determining a similarity of the logo embedding associated with the logo and a reference logo embedding associated with the at least one reference logo;
• and
• in response to a determination that the similarity fails to equal or exceed a similarity threshold, determining that the web page is associated with phishing;
• and
• performing a remedial action in response to determining that the web page is associated with phishing.