Palo Alto Networks has developed an anomalous behavior detector using the LOF algorithm with novelty detection. The method involves training the detector with stable device behaviors from network traffic data and analyzing variables across time intervals to identify anomalous device behavior. GlobalData’s report on Palo Alto Networks 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 Palo Alto Networks, IoT network security was a key innovation area identified from patents. Palo Alto Networks's grant share as of January 2024 was 62%. Grant share is based on the ratio of number of grants to total number of patents.
Anomalous behavior detection in network traffic using local outlier factor
A recently granted patent (Publication Number: US11888718B2) outlines a method for detecting anomalous device behavior in network traffic using a local outlier factor with novelty detection learner. The method involves training the model with a dataset of stable device behaviors extracted from network traffic variables, such as network address and dynamic variables like application and service. By analyzing network traffic and determining similarity measurements for variables across time intervals for specific device identifiers, the model can generate feature vectors and identify anomalous behavior based on outlier detection.
Furthermore, the patent details the use of a non-transitory, computer-readable medium with program code to execute instructions for determining similarity measurements, generating feature vectors, and inputting them into the trained model for anomaly detection. The program code also includes instructions for aggregating values of variables within time intervals, computing similarity measurements, and monitoring a set of variables indicated for aggregation. Additionally, an apparatus with a processor and machine-readable medium is described, capable of executing the program code to detect anomalous device behavior in network traffic by leveraging the trained local outlier factor with novelty detection model. This innovative approach aims to enhance cybersecurity measures by proactively identifying potential threats through the analysis of network traffic variables and device behaviors.
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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.
