Reynaldo Morillo (University of Connecticut), Justin Furuness (University of Connecticut), Cameron Morris (University of Connecticut), James Breslin (University of Connecticut), Amir Herzberg (University of Connecticut), Bing Wang (University of Connecticut)

We study and extend Route Origin Validation (ROV), the basis for the IETF defenses of interdomain routing. We focus on two important hijack attacks: _subprefix hijacks_ and _non-routed prefix hijacks_. For both attacks, we show that, with partial deployment, ROV provides disappointing security benefits. We also present _superprefix hijacks_, which completely circumvent ROV's defense for non-routed prefix hijacks, and significantly circumvents it for (announced) prefix hijacks.

We then present ROV++, a novel extension of ROV, with significantly improved security benefits even with partial adoption. For example, with uniform 5% adoption for edge ASes (ASes with no customers or peers), ROV prevents less than 5% of subprefix hijacks while ROV++ prevents more than 90% of subprefix hijacks. ROV++ also defends well against non-routed prefix attacks and the novel superprefix attacks.

We evaluated several ROV++ variants, all sharing the improvements in defense; this includes "Lite", _software-only_ variants, deployable with existing routers. Our evaluation is based on extensive simulations over the Internet topology.

We also expose an obscure yet important aspect of BGP, much amplified by ROV: _inconsistencies_ between the observable BGP path (control-plane) and the actual traffic flows (data-plane). These inconsistencies are highly relevant for security, and often lead to a challenge we refer to as _hidden hijacks_.

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Practical Blind Membership Inference Attack via Differential Comparisons

Bo Hui (The Johns Hopkins University), Yuchen Yang (The Johns Hopkins University), Haolin Yuan (The Johns Hopkins University), Philippe Burlina (The Johns Hopkins University Applied Physics Laboratory), Neil Zhenqiang Gong (Duke University), Yinzhi Cao (The Johns Hopkins University)

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Demo #10: Security of Deep Learning based Automated Lane...

Takami Sato, Junjie Shen, Ningfei Wang (UC Irvine), Yunhan Jia (ByteDance), Xue Lin (Northeastern University), and Qi Alfred Chen (UC Irvine)

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FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data

Junjie Liang (The Pennsylvania State University), Wenbo Guo (The Pennsylvania State University), Tongbo Luo (Robinhood), Vasant Honavar (The Pennsylvania State University), Gang Wang (University of Illinois at Urbana-Champaign), Xinyu Xing (The Pennsylvania State University)

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PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles

Hyungsub Kim (Purdue University), Muslum Ozgur Ozmen (Purdue University), Antonio Bianchi (Purdue University), Z. Berkay Celik (Purdue University), Dongyan Xu (Purdue University)

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