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

Background: Machine learning techniques have been widely used and demonstratepromising performance in many software security tasks such as softwarevulnerability prediction. However, the class ratio within softwarevulnerability datasets is often highly imbalanced (since the percentage ofobserved vulnerability is usually very low). Goal: To help securitypractitioners address software security data class imbalanced issues andfurther help build better prediction models with resampled datasets. Method: Weintroduce an approach called Dazzle which is an optimized version ofconditional Wasserstein Generative Adversarial Networks with gradient penalty(cWGAN-GP). Dazzle explores the architecture hyperparameters of cWGAN-GP with anovel optimizer called Bayesian Optimization. We use Dazzle to generateminority class samples to resample the original imbalanced training dataset.Results: We evaluate Dazzle with three software security datasets, i.e., Moodlevulnerable files, Ambari bug reports, and JavaScript function code. We showthat Dazzle is practical to use and demonstrates promising improvement overexisting state-of-the-art oversampling techniques such as SMOTE (e.g., with anaverage of about 60% improvement rate over SMOTE in recall among all datasets).Conclusion: Based on this study, we would suggest the use of optimized GANs asan alternative method for security vulnerability data class imbalanced issues.

Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue