Proofpoint had six patents in digitalization during Q4 2023. Proofpoint Inc filed patents related to detecting malicious sites using machine learning, detecting imposters of brand accounts on social media, and URL classification based on maliciousness scores. The technology involves using computer vision to analyze images of URLs, monitoring messages sent to brand accounts, and evaluating URLs for potential threats. GlobalData’s report on Proofpoint gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Proofpoint grant share with digitalization as a theme is 50% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Uniform resource locator classifier and visual comparison platform for malicious site detection (Patent ID: US20230421607A1)
The patent filed by Proofpoint Inc. describes a system for detecting and identifying malicious sites using machine learning. The system involves a computing platform that receives image data of a webpage, computes a computer vision vector representation of the image data, compares it to stored numeric vectors of known page elements, and inputs the result into a classifier to generate a phish classification score. If the score exceeds a threshold, the system triggers actions on a cybersecurity server, such as setting flags in a cybersecurity database. Additionally, the system can generate a screenshot database of URLs, update it when page images change, and compare image data to ancestor pages to determine similarity.
The computing platform utilizes a convolutional neural network to compute the computer vision vector representation, which is then compared to stored numeric vectors using various methods like hash table lookup or nearest neighbor search. The system can also compare image data to ancestor pages, generate object lists, and perform visual or non-visual comparisons. The cybersecurity server monitors the database for flags and performs actions accordingly, such as directing another platform to analyze the URL or displaying warnings to users. Overall, the system aims to efficiently detect and respond to malicious URLs using machine learning and image analysis techniques.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
