English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Software security is becoming a high priority for both large companies andstart-ups alike due to the increasing potential for harm that vulnerabilitiesand breaches carry with them. However, attaining robust security assurancewhile delivering features requires a precarious balancing act in the context ofagile development practices. One path forward to help aid development teams insecuring their software products is through the design and development ofsecurity-focused automation. Ergo, we present a novel approach, calledSecureReqNet, for automatically identifying whether issues in software issuetracking systems describe security-related content. Our approach consists of atwo-phase neural net architecture that operates purely on the natural languagedescriptions of issues. The first phase of our approach learns high dimensionalword embeddings from hundreds of thousands of vulnerability descriptions listedin the CVE database and issue descriptions extracted from open source projects.The second phase then utilizes the semantic ontology represented by theseembeddings to train a convolutional neural network capable of predictingwhether a given issue is security-related. We evaluated SecureReqNet byapplying it to identify security-related issues from a dataset of thousands ofissues mined from popular projects on GitLab and GitHub. In addition, we alsoapplied our approach to identify security-related requirements from acommercial software project developed by a major telecommunication company. Ourpreliminary results are encouraging, with SecureReqNet achieving an accuracy of96% on open source issues and 71.6% on industrial requirements.

Learning to Identify Security-Related Issues Using Convolutional Neural Networks