Papers
arxiv:2606.18320

TopVenues: A Reproducible Corpus and Tooling Substrate for Cybersecurity Literature Reviews

Published on Jun 16
Authors:
,
,

Abstract

Cybersecurity literature reviews require a reproducible denominator: the set of papers that a protocol includes before screening and synthesis begin. Today, that denominator is often reconstructed from publisher portals, bibliographic indices, and scholarly application programming interfaces (APIs) whose coverage, formats, and query semantics change over time. This paper presents TopVenues, an open-source system that materializes corpus construction as a versioned research artifact. TopVenues declares a venue and year scope, uses DBLP Computer Science Bibliography (DBLP) as the metadata spine, enriches records with abstracts and BibTeX entries via open scholarly APIs and publisher-specific extractors, and stores the results in a monotonic SQLite snapshot, accessible via a command-line interface (CLI), a web interface, and export paths for review workflows. The May 2026 snapshot contains 9,925 papers from 11 cybersecurity sources over 2017 to 2026, with 99.86% abstract coverage and 99.99% BibTeX coverage; keyword search over the full corpus completes in under 31 ms, and a 250-test suite validates the data-integrity invariants. The fixed denominator also enables repeatable measurement: 29.2% of 2024 to 2025 papers from the four top-ranked security conferences in our scope appear as arXiv preprints, with a median of five months before publication, and a prior-author-track-record filter yields a 16.5x precision gain at 90% recall for triaging preprints that later appear in the same venue set. TopVenues links corpus construction to auditable cybersecurity measurement by making the corpus itself executable, inspectable, and citable. The artifact is available at https://github.com/sidneibarbieri/topVenues.

Community

Sign up or log in to comment

Get this paper in your agent:

hf papers read 2606.18320
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash

Models citing this paper 0

No model linking this paper

Cite arxiv.org/abs/2606.18320 in a model README.md to link it from this page.

Datasets citing this paper 0

No dataset linking this paper

Cite arxiv.org/abs/2606.18320 in a dataset README.md to link it from this page.

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2606.18320 in a Space README.md to link it from this page.

Collections including this paper 0

No Collection including this paper

Add this paper to a collection to link it from this page.