Limin Yang, CS PhD Student at UIUC

Limin Yang (杨利民)

I'm a Senior Software Engineer at ByteDance.
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

News

07/18/2023	I passed my PhD defense. Now I'm officially Dr. Yang!
05/22/2023	Gave a talk about Jigsaw Puzzle selective backdoor at IEEE S&P 2023.
04/28/2023	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!
04/13/2023	Submitted another paper to IEEE S&P 2024! Thanks my advisor and all of our collaborators!
03/10/2023	Two papers accepted in IEEE S&P'23 (Oakland). Big thanks to everyone and see you at San Francisco!
02/21/2023	Just passed my preliminary exam. Thanks all my committee members!
02/24/2021	We release a new PE malware dataset for ML research [download link].
11/30/2020	I will be serving as a Shadow PC at IEEE S&P 2021.

Publications

[USENIX Security'24]	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
[IEEE S&P'23]	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.
[IEEE S&P'23]	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.
[USENIX Security'21]	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.
[USENIX Security'20]	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?
[IMC'19]	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.
[USENIX Security'18]	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.
[GLOBECOM'17]	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.

Journal / Workshop Papers

[DLSP'23]	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.
[DLS'21]	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.
[SafeThings'20]	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.
[PPNA'17]	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?

Internships

IBM Research	Visiting Scholar (Research Intern) , New York, US, 05/2022 – 08/2022
ByteDance	Security Engineering Intern, California, US, 05/2021 – 08/2021
Penn State	Research Intern, Pennsylvania, US, 09/2017 – 02/2018
Peking University	Exploit Intern, Beijing, China, 07/2015 – 08/2015

Awards

2021	CCS Student Conference Grant
2017	ECNU Graduate Student Overseas Research Scholarship
2013-2015	ECNU Top-notch Innovative Personnel Training Plan

Teaching

2022 Fall	CS-463 Computer Security II, UIUC, Teaching Assistant
2019 Spring	CS-4264 Principles of Computer Security, Virginia Tech, Teaching Assistant
2018 Fall	CS-3114 Data Structures and Algorithms, Virginia Tech, Teaching Assistant

Professional Services

[TDSC'23]	IEEE Transactions on Dependable and Secure Computing, Reviewer
[TSC'23]	ACM Transactions on Social Computing, Reviewer
[SECURWARE'23]	Emerging Security Information, Systems and Technologies, Technical Program Committee
[ADVCOMP'23]	Advanced Engineering Computing and Applications in Sciences, Technical Program Committee
[JISA'23]	Journal of Information Security and Applications, Reviewer
[IEEE S&P'21]	IEEE Symposium on Security and Privacy, Student PC
[Patterns'21]	Patterns (a data science journal by Cell Press), Reviewer

Upcoming Deadlines

Oakland'24	April 13, August 3, December 6, 2023. San Francisco, CA.
USENIX Sec'24	June 6, October 17, 2023; February 8, 2024. Philadelphia, PA.
CCS'23	~~January 19,~~ May 4, 2023. Copenhagen, Denmark.
NDSS'24	April 19, June 28, 2023. San Diego, CA.

Miscellaneous

I'm also a minimalism photographer: whyisyoung.com

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