Jian Cui (Indiana University Bloomington)

Twitter has been recognized as a highly valuable source for security practitioners, offering timely updates on breaking events and threat analyses. Current methods for automating event detection on Twitter rely on standard text embedding techniques to cluster tweets. However, these methods are not effective as standard text embeddings are not specifically designed for clustering security-related tweets. To tackle this, our paper introduces a novel method for creating custom embeddings that improve the accuracy and comprehensiveness of security event detection on Twitter. This method integrates patterns of security-related entity sharing between tweets into the embedding process, resulting in higher-quality embeddings that significantly enhance precision and coverage in identifying security events.

View More Papers

Programmer's Perception of Sensitive Information in Code

Xinyao Ma, Ambarish Aniruddha Gurjar, Anesu Christopher Chaora, Tatiana R Ringenberg, L. Jean Camp (Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington)

Read More

Towards Precise Reporting of Cryptographic Misuses

Yikang Chen (The Chinese University of Hong Kong), Yibo Liu (Arizona State University), Ka Lok Wu (The Chinese University of Hong Kong), Duc V Le (Visa Research), Sze Yiu Chau (The Chinese University of Hong Kong)

Read More

VETEOS: Statically Vetting EOSIO Contracts for the “Groundhog Day”...

Levi Taiji Li (University of Utah), Ningyu He (Peking University), Haoyu Wang (Huazhong University of Science and Technology), Mu Zhang (University of Utah)

Read More

BGP-iSec: Improved Security of Internet Routing Against Post-ROV Attacks

Cameron Morris (University of Connecticut), Amir Herzberg (University of Connecticut), Bing Wang (University of Connecticut), Samuel Secondo (University of Connecticut)

Read More