BlackBerry had 68 patents in cybersecurity during Q4 2023. BlackBerry Ltd filed patents in Q4 2023 for systems, methods, and software to detect domain name system tunneling, identify security risks in software code based on Software Bill of Materials, differentiate between benign and malware network data using machine learning, monitor process behavior based on software module profiles, and detect anomalies in source code by analyzing attribute values of code commits. GlobalData’s report on BlackBerry gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
Smarter leaders trust GlobalData
BlackBerry grant share with cybersecurity as a theme is 33% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Domain name system tunneling detection (Patent ID: US20230388322A1)
The patent filed by BlackBerry Ltd. describes systems, methods, and software for detecting Domain Name System Tunneling (DNST). The method involves receiving DNS requests, processing them using a machine learning model to identify suspicious requests, and then using a statistical analysis model to determine if these suspicious requests are potentially malicious. The machine learning model involves determining strings corresponding to subdomains in DNS requests, calculating entropy vectors, and identifying suspicious requests based on these vectors. The statistical analysis model involves determining suspicious domains, calculating ratios of unique subdomains to total requests for each domain, identifying potentially malicious domains based on these ratios, and determining if the suspicious requests contain these potentially malicious domains.
The patent also includes claims for a computer-implemented system and a non-transitory computer-readable medium that perform similar operations as the method described. The system includes computers and memory devices storing instructions for receiving DNS requests, processing them with machine learning and statistical analysis models to detect potentially malicious requests. The non-transitory computer-readable medium stores instructions for executing operations like determining strings and entropy vectors, performing Fourier transforms, and analyzing ratios of unique subdomains to total requests to identify potentially malicious domains. Overall, the patent outlines a comprehensive approach to detecting DNST using machine learning and statistical analysis models to enhance cybersecurity measures.
To know more about GlobalData’s detailed insights on BlackBerry, buy the report here.
Premium Insights
From
The gold standard of business intelligence.
Blending expert knowledge with cutting-edge technology, GlobalData’s unrivalled proprietary data will enable you to decode what’s happening in your market. You can make better informed decisions and gain a future-proof advantage over your competitors.
GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
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.
