activity
20222026
most citedDarkBERT: A Language Model for the Dark Side of the Internet

3 citations · 8 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2026

Universal NER v2: Towards a Massively Multilingual Named Entity Recognition Benchmark

Terra Blevins, Stephen Mayhew, Marek Šuppa +11

While multilingual language models promise to bring the benefits of LLMs to speakers of many languages, gold-standard evaluation benchmarks in most languages to interrogate these a…

cs.CL2025★ 1 cited

Covering Cracks in Content Moderation: Delexicalized Distant Supervision for Illicit Drug Jargon Detection

Minkyoo Song, Eugene Jang, Jaehan Kim +1

In light of rising drug-related concerns and the increasing role of social media, sales and discussions of illicit drugs have become commonplace online. Social media platforms host…

cs.CL2024

Improbable Bigrams Expose Vulnerabilities of Incomplete Tokens in Byte-Level Tokenizers

Eugene Jang, Kimin Lee, Jin-Woo Chung +2

Tokenization is a crucial step that bridges human-readable text with model-readable discrete tokens. However, recent studies have revealed that tokenizers can be exploited to elici…

cs.CR2024★ 1 cited

Tweezers: A Framework for Security Event Detection via Event Attribution-centric Tweet Embedding

Jian Cui, Hanna Kim, Eugene Jang +6

Twitter is recognized as a crucial platform for the dissemination and gathering of Cyber Threat Intelligence (CTI). Its capability to provide real-time, actionable intelligence mak…

cs.CR2024

Ignore Me But Don't Replace Me: Utilizing Non-Linguistic Elements for Pretraining on the Cybersecurity Domain

Eugene Jang, Jian Cui, Dayeon Yim +4

Cybersecurity information is often technically complex and relayed through unstructured text, making automation of cyber threat intelligence highly challenging. For such text domai…

cs.CL2023★ 3 cited

DarkBERT: A Language Model for the Dark Side of the Internet

Youngjin Jin, Eugene Jang, Jian Cui +3

Recent research has suggested that there are clear differences in the language used in the Dark Web compared to that of the Surface Web. As studies on the Dark Web commonly require…

cs.CR2023★ 1 cited

DRAINCLoG: Detecting Rogue Accounts with Illegally-obtained NFTs using Classifiers Learned on Graphs

Hanna Kim, Jian Cui, Eugene Jang +4

As Non-Fungible Tokens (NFTs) continue to grow in popularity, NFT users have become targets of phishing attacks by cybercriminals, called \textit{NFT drainers}. Over the last year,…

cs.CL2022★ 2 cited

Shedding New Light on the Language of the Dark Web

Youngjin Jin, Eugene Jang, Yongjae Lee +2

The hidden nature and the limited accessibility of the Dark Web, combined with the lack of public datasets in this domain, make it difficult to study its inherent characteristics s…