Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from VLDB 2026 / reviewers in the wild / expert

Euan Goodbrand

dblp:420/7847 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0001-2926-8452ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Malware analysis · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Malware analysis
malware classification		 0.912025
Deep Learning from Imperfectly Labeled Malware Data · CCS 2025
Malware analysis
malware detection		 0.912025
Deep Learning from Imperfectly Labeled Malware Data · CCS 2025

Methods — techniques the papers use, named apart from their topics

noisy label learning · 0.9deep learning · 0.9
YearPublicationVenuePosition
2025 Deep Learning from Imperfectly Labeled Malware Data
abstract
Deep learning approaches have achieved remarkable performance in malware classification and detection. However, their success relies on the availability of large, accurately labeled datasets: a critical yet challenging requirement in the malware domain. In practice, most malware datasets are automatically labeled using outputs from antivirus engines, a process that often introduces significant label noise. Such imperfections can severely degrade the performance and generalizability of deep learning models.
Fahad Alotaibi, Euan Goodbrand, Sergio Maffeis
 CCS2