Johnathan Wilkes, John Anny (Palo Alto Networks)

By embracing automation, organizations can transcend manual limitations to reduce mean time to response and address exposures consistently across their cybersecurity infrastructure. In the dynamic realm of cybersecurity, swiftly addressing externally discovered exposures is paramount, as each represents a ticking time bomb. A paradigm shift towards automation to enhance speed, efficiency, and uniformity in the remediation process is needed to answer the question, "You found the exposure, now what?". Traditional manual approaches are not only time-consuming but also prone to human error, underscoring the need for a comprehensive, automated solution. Acknowledging the diversity of exposures and the array of security tools, we will propose how to remediate common external exposures, such as open ports and dangling domains. The transformative nature of this shift is crucial, particularly in the context of multiple cloud platforms with distinct data enrichment and remediation capabilities.

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Connecting the Dots in the Sky: Website Fingerprinting in...

Prabhjot Singh (University of Waterloo), Diogo Barradas (University of Waterloo), Tariq Elahi (University of Edinburgh), Noura Limam (University of Waterloo)

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LoRDMA: A New Low-Rate DoS Attack in RDMA Networks

Shicheng Wang (Tsinghua University), Menghao Zhang (Beihang University & Infrawaves), Yuying Du (Information Engineering University), Ziteng Chen (Southeast University), Zhiliang Wang (Tsinghua University & Zhongguancun Laboratory), Mingwei Xu (Tsinghua University & Zhongguancun Laboratory), Renjie Xie (Tsinghua University), Jiahai Yang (Tsinghua University & Zhongguancun Laboratory)

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DeepGo: Predictive Directed Greybox Fuzzing

Peihong Lin (National University of Defense Technology), Pengfei Wang (National University of Defense Technology), Xu Zhou (National University of Defense Technology), Wei Xie (National University of Defense Technology), Gen Zhang (National University of Defense Technology), Kai Lu (National University of Defense Technology)

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SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems

Guangke Chen (ShanghaiTech University), Yedi Zhang (National University of Singapore), Fu Song (Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences)

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