Zscaler had six patents in artificial intelligence during Q1 2024. Zscaler Inc’s patents filed in Q1 2024 focus on methods for analyzing packets associated with files to determine maliciousness, dynamic applicative session separation and grouping, and inline monitoring of production traffic using machine learning models to classify traffic as malicious or benign. These systems aim to enhance security and efficiency in cloud-based environments. GlobalData’s report on Zscaler gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Zscaler grant share with artificial intelligence as a theme is 50% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Systems and methods for intelligent machine learning-based malware detection (Patent ID: US20240045963A1)
The patent filed by Zscaler Inc. describes a method for analyzing packets associated with a file to determine the probability of maliciousness. The method involves extracting features from the content of packets, applying a machine learning model to assess maliciousness probability, and labeling packets based on predefined thresholds. Additionally, the method includes storing packets with uncertain maliciousness, converting packet content into digital representations, and labeling the overall file based on the maliciousness probability.
Furthermore, the patent outlines the use of various file types such as portable executable (PE) files, PDF files, DLLs, JavaScript files, HTML files, and Microsoft Office files in the analysis process. The machine learning model utilized in the method consists of decision trees, and features extracted from packet content include n-gram features, entropy features, and domain features. Overall, the method provides a systematic approach to identifying potentially malicious packets within a file by leveraging machine learning and feature extraction 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.
