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Limin Yang (杨利民)
I'm joining TikTok US as a software engineer in August 2023.
I got my PhD from UIUC Computer Science.
During my PhD, I focus on machine learning security and Internet measurement.
My advisor is Prof. Gang Wang.
Contact: liminy2@illinois.edu
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07/18/2023
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I passed my PhD defense. Now I'm officially Dr. Yang!
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05/22/2023
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Gave a talk about Jigsaw Puzzle selective backdoor at IEEE S&P 2023.
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04/28/2023
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Will be giving a talk about CADE at the Noise Lab of UChicago this Friday (04/28).
Thanks for the invitation from Shinan Liu and Prof. Nick Feamster!
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04/13/2023
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Submitted another paper to IEEE S&P 2024! Thanks my advisor and all of our collaborators!
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03/10/2023
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Two papers accepted in IEEE S&P'23 (Oakland). Big thanks to everyone and see you at San Francisco!
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02/21/2023
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Just passed my preliminary exam. Thanks all my committee members!
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02/24/2021 |
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 IEEE S&P 2021.
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[USENIX Security'24]
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True Attacks, Attack Attempts, or Benign Triggers? An Empirical Measurement of Network Alerts in a Security Operations Center
Limin Yang*, Zhi Chen*, Chenkai Wang, Zhenning Zhang, Sushruth Booma, Phuong Cao, Constantin Adam, Alex Withers, Zbigniew Kalbarczyk, Ravishankar K. Iyer, Gang Wang
PDF
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[IEEE S&P'23]
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Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifier
Limin Yang, Zhi Chen, Jacopo Cortellazzi, Feargus Pendlebury, Kevin Tu, Fabio Pierazzi, Lorenzo Cavallaro, Gang Wang
PDF /
Supplementary PDF /
Code /
Slides /
Video
TL;DR: Jigsaw Puzzle backdoor attack can selectively protect one specific malware family but not other malware, leaving smaller footprints and thus bypassing many existing backdoor defenses.
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[IEEE S&P'23]
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Everybody's Got ML, Tell Me What Else You Have: Practitioners' Perception of ML-Based Security Tools and Explanations
Jaron Mink, Hadjer Benkraouda, Limin Yang, Arridhana Ciptadi, Ali Ahmadzadeh, Daniel Votipka, Gang Wang
PDF /
Supplementary PDF
TL;DR: Semi-structured interviews with 18 practioners to understand their perspectives on ML and ML explanation.
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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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[DLSP'23]
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Is It Overkill? Analyzing Feature-Space Concept Drift in Malware Detectors
Zhi Chen, Zhenning Zhang, Zeliang Kan, Limin Yang, Jacopo Cortellazzi, Feargus Pendlebury, Fabio Pierazzi, Lorenzo Cavallaro, Gang Wang
PDF
TL;DR: Data-space drift is the dominating contributor to the model degradation over time while feature-space drift has little to no impact.
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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
PDF /
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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IBM Research
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Visiting Scholar (Research Intern) , New York, US, 05/2022 – 08/2022
Mentors: Dr. Muhammed Fatih Bulut and Dr. Constantin Adam
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TikTok
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Security Engineering Intern, California, US, 05/2021 – 08/2021
Mentors: Dr. Dazhuo Li, Dr. Hamed Ashouri, and Dr. Hui Cao
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Penn State
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Research Intern, Pennsylvania, US, 09/2017 – 02/2018
Mentors: Dr. Xinyu Xing and Dr. Gang Wang
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Peking University
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Exploit Intern, Beijing, China, 07/2015 – 08/2015
Mentors: Dr. Yu Ding and Dr. Xinhui Han.
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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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2022 Fall |
CS-463 Computer Security II, UIUC, Teaching Assistant
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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'24
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April 13, August 3, December 6, 2023. San Francisco, CA.
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USENIX Sec'24
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June 6, October 17, 2023; February 8, 2024. Philadelphia, PA.
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CCS'23
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January 19, May 4, 2023. Copenhagen, Denmark.
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NDSS'24
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April 19, June 28, 2023. San Diego, CA.
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