Yuejie Wang (Peking University), Qiutong Men (New York University), Yongting Chen (New York University Shanghai), Jiajin Liu (New York University Shanghai), Gengyu Chen (Carnegie Mellon University), Ying Zhang (Meta), Guyue Liu (Peking University), Vyas Sekar (Carnegie Mellon University)

Enterprises are increasingly outsourcing network management (e.g., troubleshooting routing issues) to reduce cost and improve efficiency, either by hiring third-party contractors or by outsourcing to third-party vendors. Unfortunately, recent events have shown that this outsourcing model has become a new source of network incidents in customer networks. In this work, we argue that a risk-aware outsourcing approach is needed that enables customers to measure and assess risk transparently and make informed decisions to minimize harm. We first concretely define the notion of risk in the context of outsourced network management and then present an end-to-end framework, called Heimdall, which enables enterprises to assess, monitor, and respond to risk. Heimdall automatically builds a dependency graph to accurately assess the risk of an outsourced task, and uses a fine-grained reference monitor to monitor and mitigate potential risks during operation. Our expert validation results show that Heimdall effectively controls risk for outsourced network operations, resolving 92% of practical issues at the minimal risk level while incurring only a marginal timing overhead of approximately 7%.

View More Papers

VulShield: Protecting Vulnerable Code Before Deploying Patches

Yuan Li (Zhongguancun Laboratory & Tsinghua University), Chao Zhang (Tsinghua University & JCSS & Zhongguancun Laboratory), Jinhao Zhu (UC Berkeley), Penghui Li (Zhongguancun Laboratory), Chenyang Li (Peking University), Songtao Yang (Zhongguancun Laboratory), Wende Tan (Tsinghua University)

Read More

SCAMMAGNIFIER: Piercing the Veil of Fraudulent Shopping Website Campaigns

Marzieh Bitaab (Arizona State University), Alireza Karimi (Arizona State University), Zhuoer Lyu (Arizona State University), Adam Oest (Amazon), Dhruv Kuchhal (Amazon), Muhammad Saad (X Corp.), Gail-Joon Ahn (Arizona State University), Ruoyu Wang (Arizona State University), Tiffany Bao (Arizona State University), Yan Shoshitaishvili (Arizona State University), Adam Doupé (Arizona State University)

Read More

A Method to Facilitate Membership Inference Attacks in Deep...

Zitao Chen (University of British Columbia), Karthik Pattabiraman (University of British Columbia)

Read More

A Key-Driven Framework for Identity-Preserving Face Anonymization

Miaomiao Wang (Shanghai University), Guang Hua (Singapore Institute of Technology), Sheng Li (Fudan University), Guorui Feng (Shanghai University)

Read More