Jonghoon Kwon (ETH), Taeho Lee (ETH), Claude Hähni (ETH), Adrian Perrig (ETH)

Network isolation is a critical modern Internet service. To date, network operators have created a logical network of distributed systems to provide communication isolation between different parties. However, the current network isolation is limited in scalability and flexibility. It limits the number of virtual networks and it only supports isolation at host (or virtual-machine) granularity. In this paper, we introduce Scalable Virtual Local Area Networking (SVLAN) that scales to a large number of distributed systems and offers improved flexibility in providing secure network isolation. With the notion of destination-driven reachability and packet-carrying forwarding state, SVLAN not only offers communication isolation but isolation can be specified at different granularities, e.g., per-application or per-process. Our proof-of-concept SVLAN implementation demonstrates its feasibility and practicality for real-world applications.

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Menghao Zhang (Tsinghua University), Guanyu Li (Tsinghua University), Shicheng Wang (Tsinghua University), Chang Liu (Tsinghua University), Ang Chen (Rice University), Hongxin Hu (Clemson University), Guofei Gu (Texas A&M University), Qi Li (Tsinghua University), Mingwei Xu (Tsinghua University), Jianping Wu (Tsinghua University)

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BLAG: Improving the Accuracy of Blacklists

Sivaramakrishnan Ramanathan (University of Southern California/Information Sciences Institute), Jelena Mirkovic (University of Southern California/Information Sciences Institute), Minlan Yu (Harvard University)

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Soroush Karami (University of Illinois at Chicago), Panagiotis Ilia (University of Illinois at Chicago), Konstantinos Solomos (University of Illinois at Chicago), Jason Polakis (University of Illinois at Chicago)

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On the Resilience of Biometric Authentication Systems against Random...

Benjamin Zi Hao Zhao (University of New South Wales and Data61 CSIRO), Hassan Jameel Asghar (Macquarie University and Data61 CSIRO), Mohamed Ali Kaafar (Macquarie University and Data61 CSIRO)

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