Lannan Luo

I am an Assistant Professor in the Department of Computer Science and Engineering at University of South Carolina. I received my PhD in the College of Information Sciences and Technology from Penn State University in August, 2017, working under the supervision of Dr. Peng Liu. The easiest way to contact me is through email at lluo (at)

My research mainly focuses on software and systems security. My reserach interests include mobile security, IoT security, malware analysis, vulnerability analysis, programming languages, software engineering, and deep learning. My research approaches are mainly empirical in tandem with formal methods, combining symbolic execution, theorem proving, taint analysis, control flow analysis, data flow analysis, reverse engineering, data mining, and deep learning.

To prospective students:
I am looking for self-motivated Ph.D. students, master students, summer interns, and visiting scholars. For more details, please visit here.


I am serving as the publicity co-chair for CNS 2018. Welcome to submit your papers here.



10/31/17 Our work that builds resilient repackaging-detection capability into mobile apps through binary code instrumentation is accepted at CGO'18.

06/19/17 We have opensourced our tool for Symbolic Execution of Android Framework (presented in MobiSys'17).

02/25/17 Symbolic Execution of Android Framework for Vulnerability Discovery and Exploit Generation is accepted at MobiSys'17.

12/22/16 Our paper "Semantics-Based Obfuscation-Resilient Binary Code Similarity Comparison with Applications to Software and Algorithm Plagiarism Detection" is accepted at TSE'17.

02/29/16 SSN, which builds a reliable and stealthy repackage-proofing capability into Android apps, is accepted at DSN'16.

01/22/16 SolMiner, which automatically mines distinct solutions from programs, is accepted at ICSE'16.

11/21/14 Best Paper Award Nomination: Our FSE ’14 paper was nominated for the best paper award. FSE is one of two finest conferences in the field of software engineering.

06/16/14 CoP, a binary-oriented, obfuscation-resilient software plagiarism detection method, is accepted at FSE'14.


Contact Information

Rm 2245
Storey Innovation Center
Computer Science and Engineering
University of South Carolina
Email: lluo (at) cse (dot) sc (dot) edu