SentinelOne. has been granted a patent for a real-time malware detection system. The method involves monitoring program operations, building stateful models, and analyzing behaviors to identify potential malware, utilizing a hierarchical structure and scoring system to assess the likelihood of threats based on observed behaviors. GlobalData’s report on SentinelOne 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 SentinelOne, was a key innovation area identified from patents. SentinelOne's grant share as of June 2024 was 56%. Grant share is based on the ratio of number of grants to total number of patents.
Real-time malware detection using behavior-based analysis
The granted patent US12026257B2 outlines a computer-implemented method and system for conducting behavior-based analysis of program execution within an operating system. The method involves monitoring operations performed by a program in a live environment through kernel filter drivers and callback functions. This monitoring encompasses both user space and kernel space operations, generating event data for each monitored operation. The event data is then normalized into a logical structure, allowing for the construction of a stateful model that reflects the hierarchical relationships and attributes of the monitored operations. The system analyzes this model to identify behaviors associated with the program execution and applies a scoring mechanism to assess the likelihood of malware presence based on these behaviors.
Additionally, the patent describes features for real-time updates to the stateful model in response to new events, outputting representations of identified behaviors, and storing these behaviors in a behavioral profile database. The system can be implemented in a cloud-based environment or distributed across multiple computers, facilitating concurrent operation of various programs. The analysis of behaviors may also include aggregating data and determining behavior patterns, ultimately enabling the identification of potential malware within the program execution. This comprehensive approach aims to enhance the security and reliability of software applications by providing detailed insights into their operational behaviors.
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