Debricked Ab

Micro Focus Lcc

Carl Emil Orm Wåreus

Anton Duppils

Per Filip Heden

Magnus Tullberg

Nils Valdemar Barr Zeilon

A database stores, for each of a number of software packages, a software package embedding representing the software package. The database stores, for each software package, code block embeddings respectively representing code blocks of the software package. The database stores, for each software package, functionality embeddings respectively representing functionality clusters into which the code block embeddings representing the code blocks of the software package have been clustered. A query embedding representing a query is generated, and used to query the database to identify a relevant code block within a relevant software package for the query.

Identification of relevant code block within relevant software package for a query

A method () for linking a common vulnerability and exposure, CVE, () with at least one synthetic common platform enumeration, CPE, () wherein the CVE () comprises a summary of a vulnerability, is disclosed. The method () comprising: receiving (S) the summary of the CVE () from a vulnerability database, VD, (); extracting (S) information from the summary of the CVE () using a Natural Language Processing, NLP, model; building (S) at least one synthetic CPE () based on the extracted information; and linking (S) the CVE () with the at least one synthetic CPE ().

Method for linking a CVE with at least one synthetic CPE

Open-source software is prevalent in the development of new technologies. Monitoring software updates for vulnerabilities is expensive and time consuming. Online discussions surrounding new software updates can often provide vital information regarding emerging risks. It is presented a novel approach for automating surveillance of software through the use of natural language processing methods on open-source issues. Further, the potential of virtual adversarial training, a popular semi-supervised learning technique, is used to leverage the vast amounts of unlabeled data available to achieve improved performance. On industry data, it is found that a hierarchical attention network with virtual adversarial training that utilizes the innate document structure to encapsulate the text can be used with good results.

Growing community of inventors

Malmo, Sweden

Carl Emil Orm Wåreus