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Limin Yang (杨利民)
I am a fourth-year Ph.D. candidate of Computer Science at UIUC.
I focus on machine learning security, measurement, and explainable AI.
My advisor is Prof. Gang Wang.
I also work closely with Prof. Xinyu Xing and Prof. Linhai Song from Penn State.
Contact: liminy2@illinois.edu
Office: Room 4111, Thomas M. Siebel Center, Urbana, IL 61801. Work from home at Los Angeles.
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02/01/2022
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Submitted a paper to USENIX Security 2022! Thanks a lot to my advisor and collaborators!
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05/17/2021 |
First day at TikTok (ByteDance) as a Security Engineering Intern.
Thanks for the opportunity, Dr. Yu Ding and Dr. Hui Cao!
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04/09/2021 |
Just passed my qualifier exam. Now I'm officially a Ph.D. candidate!
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02/24/2021 |
Our paper got accepted to the DLS workshop. We release a new PE malware dataset for ML research [download link].
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11/30/2020 |
I will be serving as a Shadow PC at Oakland 2021.
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09/30/2020 |
Our USENIX Security'21 paper got accepted! Thanks for everyone who helped me!
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Publications
Conference Papers
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[USENIX Security'21]
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CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Limin Yang, Wenbo Guo, Qingying Hao, Arridhana Ciptadi, Ali Ahmadzadeh, Xinyu Xing, Gang Wang
PDF /
Supplementary PDF /
Code /
Slides /
Video /
Artifact Evaluated
TL;DR: Contrastive learning can help detect drifting samples from previously unseen classes and distance-based explanation can find out why such drifting samples are different from existing classes.
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[USENIX Security'20]
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Measuring and Modeling the Label Dynamics of Online Anti-Malware Engines
Shuofei Zhu, Jianjun Shi, Limin Yang, Boqin Qin, Ziyi Zhang, Linhai Song, Gang Wang
PDF /
Data /
Artifact Evaluated
TL;DR: How to aggregate VirusTotal labels more properly?
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[IMC'19]
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Opening the Blackbox of VirusTotal: Analyzing Online Phishing Scan Engines
Peng Peng, Limin Yang, Linhai Song, Gang Wang
PDF /
Data
TL;DR: Control simple phishing sites to see how bad and inconsistent of the URL detection provided by VirusTotal and security vendors' own APIs.
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[USENIX Security'18]
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Understanding the Reproducibility of Crowd-reported Security Vulnerabilities
Dongliang Mu, Alejandro Cuevas, Limin Yang, Hang Hu, Xinyu Xing, Bing Mao, Gang Wang
PDF /
Data
TL;DR: Provide quantitive evidence on the prevalence of missing information in vulnerability reports
and low reproducibility by manually reproduce 368 memory corruption bugs.
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[GLOBECOM'17]
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VulDigger: A Just-in-Time and Cost-Aware Tool for Digging Vulnerability-Contributing Changes
Limin Yang, et al.
PDF
TL;DR: Use metadata and heuristics to predict whether a git commit introduced a vulnerability.
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Journal / Workshop Papers
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[DLS'21]
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BODMAS: An Open Dataset for Learning based Temporal Analysis of PE Malware
Limin Yang, Arridhana Ciptadi, Ihar Laziuk, Ali Ahmadzadeh, Gang Wang
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Slides /
Data /
Code
TL;DR: We collaborate with Blue Hexagon
to release an open Windows PE malware dataset with timestamped metadata as well as curated malware family labels.
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[SafeThings'20]
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A Case Study of the Security Vetting Process of Smart-home Assistant Applications
Hang Hu, Limin Yang, Shihan Lin, Gang Wang
PDF
TL;DR: Verify that replay attack and SQL injection are feasible in Amazon Alexa / Google Home apps.
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[PPNA'17]
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Characterizing User Behaviors in Location-based Find-and-Flirt Services: Anonymity and Demographics
Minhui Xue, Limin Yang, Keith W. Ross, Haifeng Qian
PDF
TL;DR: Who used the WeChat "People Nearby" feature more often?
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2021 |
CCS Student Conference Grant
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2017 |
ECNU Graduate Student Overseas Research Scholarship
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2013-2015 |
ECNU Top-notch Innovative Personnel Training Plan
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2019 Spring |
CS-4264 Principles of Computer Security, Virginia Tech, Teaching Assistant
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2018 Fall |
CS-3114 Data Structures and Algorithms, Virginia Tech, Teaching Assistant
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Oakland'22
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April 15, August 19, December 2, 2021. San Francisco, CA.
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USENIX Sec'22
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June 8, October 12, 2021; February 1, 2022. Boston, MA.
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CCS'22
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January 14, May 2, 2022. Los Angeles, CA.
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IMC'22
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May 11 (Abstract), May 18 (Full Paper) 2022. Nice, France.
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Feel free to steal this website's source code, just add a link back to my website and credit Jon Barron and me. Please remove any code that contains "UA-45384067-2" and "GTM-N2T82H2" as they are my Google Analytic tracking ID and you do not want on your own website.
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