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

Parrot-Trained Adversarial Examples: Pushing the Practicality of Black-Box Audio...

Rui Duan (University of South Florida), Zhe Qu (Central South University), Leah Ding (American University), Yao Liu (University of South Florida), Zhuo Lu (University of South Florida)

Read More

WIP: Security Vulnerabilities and Attack Scenarios in Smart Home...

Haoqiang Wang (Chinese Academy of Sciences, University of Chinese Academy of Sciences, Indiana University Bloomington), Yichen Liu (Indiana University Bloomington), Yiwei Fang, Ze Jin, Qixu Liu (Chinese Academy of Sciences, University of Chinese Academy of Sciences, Indiana University Bloomington), Luyi Xing (Indiana University Bloomington)

Read More

A Cross-Verification Approach with Publicly Available Map for Detecting...

Takami Sato, Ningfei Wang (University of California, Irvine), Yueqiang Cheng (NIO Security Research), Qi Alfred Chen (University of California, Irvine)

Read More

WIP: Body Posture Analysis as an Objective Measurement for...

Cherin Lim, Tianhao Xu, Prashanth Rajivan (University of Washington)

Read More