The Royal Bank of Canada has patented a method using message and screenshot metadata to detect electronic data exfiltration. By matching screenshots to sent messages and applying unsupervised machine learning, potential anomalies are identified, indicating possible data breaches. Multiple screenshots are captured to enhance accuracy. GlobalData’s report on Royal Bank of Canada gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Royal Bank of Canada, Social commerce was a key innovation area identified from patents. Royal Bank of Canada's grant share as of May 2024 was 61%. Grant share is based on the ratio of number of grants to total number of patents.
Identifying electronic data exfiltration using screenshots and message metadata
A recently granted patent (Publication Number: US11989310B2) outlines a method for detecting anomalies in electronic messages by analyzing message metadata and screenshot metadata. The method involves matching screenshots with corresponding messages, determining anomaly scores using unsupervised machine learning, and setting anomaly thresholds. The patent also details various factors that can influence anomaly scores, such as sender behavior, recipient identity, file attachments, message size, and time differences between message sending and screenshot capture. Additionally, the patent describes the use of an isolation forest for machine learning and the potential action of quarantining messages based on anomaly scores.
Furthermore, the patent extends to a system comprising processors and memory storing executable code to implement the anomaly detection method. The system involves obtaining message and screenshot metadata, matching screenshots with messages, determining anomaly scores using unsupervised machine learning, and setting anomaly thresholds. The system also emphasizes the importance of capturing multiple screenshots before message sending, with the most recent screenshot used for event generation. This innovative approach to anomaly detection in electronic messages showcases the integration of machine learning techniques and metadata analysis to enhance security measures and prevent potential threats in communication channels.
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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.
