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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Cyber-security vulnerabilities are usually published in form of short naturallanguage descriptions (e.g., in form of MITRE's CVE list) that over time arefurther manually enriched with labels such as those defined by the CommonVulnerability Scoring System (CVSS). In the Vulnerability AI (Analytics andIntelligence) project, we investigated different types of semanticvulnerability embeddings based on natural language processing (NLP) techniquesto obtain a concise representation of the vulnerability space. We alsoevaluated their use as a foundation for machine learning applications that cansupport cyber-security researchers and analysts in risk assessment and otherrelated activities. The particular applications we explored and brieflysummarize in this report are clustering, classification, and visualization, aswell as a new logic-based approach to evaluate theories about the vulnerabilityspace.

Vulnerability Clustering and other Machine Learning Applications of Semantic Vulnerability Embeddings