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.

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TALISMAN: Tamper Analysis for Reference Monitors

Frank Capobianco (The Pennsylvania State University), Quan Zhou (The Pennsylvania State University), Aditya Basu (The Pennsylvania State University), Trent Jaeger (The Pennsylvania State University, University of California, Riverside), Danfeng Zhang (The Pennsylvania State University, Duke University)

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MOCK: Optimizing Kernel Fuzzing Mutation with Context-aware Dependency

Jiacheng Xu (Zhejiang University), Xuhong Zhang (Zhejiang University), Shouling Ji (Zhejiang University), Yuan Tian (UCLA), Binbin Zhao (Georgia Institute of Technology), Qinying Wang (Zhejiang University), Peng Cheng (Zhejiang University), Jiming Chen (Zhejiang University)

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Transpose Attack: Stealing Datasets with Bidirectional Training

Guy Amit (Ben-Gurion University), Moshe Levy (Ben-Gurion University), Yisroel Mirsky (Ben-Gurion University)

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Group-based Robustness: A General Framework for Customized Robustness in...

Weiran Lin (Carnegie Mellon University), Keane Lucas (Carnegie Mellon University), Neo Eyal (Tel Aviv University), Lujo Bauer (Carnegie Mellon University), Michael K. Reiter (Duke University), Mahmood Sharif (Tel Aviv University)

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