Veracode has patented a system for identifying software flaws using a stacked classifier model ensemble, improving precision by 54.55%. The method involves k-fold cross validation, training a set of classifiers, and a logistic regression model to generate probabilities for software components. The system updates a database with the probability of flaws for each component. GlobalData’s report on Veracode 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 Veracode, Social data privacy protection was a key innovation area identified from patents. Veracode's grant share as of April 2024 was 47%. Grant share is based on the ratio of number of grants to total number of patents.
Software vulnerability detection using classifier ensemble and logistic regression
A recently granted patent (Publication Number: US11899800B2) outlines a method for identifying vulnerabilities or flaws in software components using a combination of classifiers and logistic regression models. The method involves training a set of classifiers using k-fold cross-validation and then utilizing probability vectors generated from these classifiers to train a logistic regression model. By inputting a software development artifact-derived vector into this ensemble, probabilities are generated to determine the presence of vulnerabilities or flaws in the software component. These probabilities are then used to update a database associated with the identity of the software component, providing valuable insights for developers and security professionals.
Furthermore, the patent describes the use of natural language processing to extract tokens related to vulnerabilities or flaws from software development artifacts such as commit messages or bug reports. By associating labels indicating the presence of vulnerabilities with the software components, the method enhances the accuracy of vulnerability detection. The patent also includes instructions for a computer-readable medium and an apparatus with a processor to execute the method, emphasizing the practical application of this innovative approach in the field of software security. By combining the outputs of trained classifiers and logistic regression models, the ensemble generated by the method offers a comprehensive assessment of software vulnerabilities, enabling proactive measures to be taken to enhance software security and reliability based on the probabilities generated.
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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.
