|Sen Chen (Willson) Ph.D. Candidate and Research Assistant Nanyang Technological University, Singapore East China Normal University, China ecnuchensen AT gmail.com chensen AT ntu.edu.sg|
Sen Chen is a final year Ph.D. student focusing primarily on areas of mobile app security (e.g., malware and vulnerability detection) and mobile app testing (e.g., Android framework-specific exceptions). He received an ACM SIGSOFT Distinguished Paper Award at ICSE 2018.
I'm currently advised by Professor Lihua Xu (NYU Shanghai). In addition, I owe a big thanks to Professor Haojin Zhu (SJTU) for leading me into the field of mobile security.
I am also a Research Assistant at School of Computer Science and Engineering of Nanyang Technological University in October 2016 to June 2019. During my visit in NTU, I am advised by Professor Liu Yang.
My research focuses on developing effective techniques and tools to improve mobile app security and quality:
Mobile APP Security:
Android Malware Detection: StormDroid (AsiaCCS'16), Begonia (CCS'16)
Adversarial Malware Detection: KuafuDet (MobiCom'16, Computers & Security'17)
Android Vulnerability Detection: AUSERA (FSE'18, TDSC'19)
Android Grayware Detection: GrayDet
Android Fake App Analysis: Ecosystem and Trend
Android GUI Attack: GUI-Squatting Attack (TIFS'19)
Mobile APP Analysis and Development:
Android App Development (e.g., competitive analysis, code review, and code generation):
StoryDroid (ICSE'19) Android App Exception Analysis: Exlocator (ICSE'18), APEChecker (ASE'18) Formal Verification: Eunomia (APSEC'16)
Android Malware: I worked on Android malware detection and classification (StormDroid) using machine learning (both static and dynamic features with statistical metrics) (refer to AsiaCCS'16). I've developed KuafuDet (refer to MobiCom'16, Elsevier Computers & Security'17) for Android malware detection using machine learning in adversarial environment. We examine how machine-learning classifiers (e.g., DroidAPIMiner, DREBIN, and MaMaDroid) can be misled under different threat models.
Android Vulnerability: I worked on security vulnerability detection (AUSERA) in security-critical FinTech applications (e.g., mobile banking apps and payment apps). We investigated the characteristics of these vulnerabilities, and the changing trend over time.
Android App Development: We proposed a system, StoryDroid, to automatically generate the storyboard for Android apps, and assist different roles to review apps efficiently. Specifically, it extracts the activity transition graph and leverages static analysis techniques to render UI pages to visualize the storyboard with the rendered pages. The mapping relations between UI pages and the corresponding implementation code (e.g., layout code, activity code, and method hierarchy) are also provided to users. Such a storyboard benefits different roles (i.e., PMs, UI designers, and developers) in the app development process.
Android App Analysis/Testing: We conducted a large-scale empirical study to characterize framework-specific exceptions in Android apps, and implemented ExLocator (refer to ICSE'18), an exception localization tool, for Android applications. We proposed APEChecker (refer to ASE'18), a technique to efficiently manifest aysnc programming errors (APEs) in Android apps.
Ph.D. Student, Computer Science and Technology, East China Normal University, September 2014 - June 2019
Research Assistant, School of Computer Science and Engineering, NTU, January 2018 - June 2019
Visiting Ph.D. Student, School of Computer Science and Engineering, NTU, October 2016 - December 2017
B.S. Student, Software Engineering, Harbin Normal University, September 2010 - June 2014
8. Sen Chen, Lingling Fan, Chunyang Chen, Ting Su, Wenhe Li, Yang Liu, and Lihua Xu, "StoryDroid: Automated Generation of Storyboard for Android Apps", In Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE2019), Montréal, QC, Canada, 2019. (acceptance rate: 109/529 = 20.6%)
7. Sen Chen, Ting Su, Lingling Fan, Guozhu Meng, Minhui Xue, Yang Liu, and Lihua Xu, "Are Mobile Banking Apps Secure? What Can be Improved?", In Proceedings of the 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE2018), Lake Buena Vista, Florida, United States, 2018
6. Lingling Fan, Ting Su, Sen Chen, Guozhu Meng, Yang Liu, Lihua Xu and Geguang Pu, "Efficiently Manifesting Asynchronous Programming Errors in Android Apps", In Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE2018), Montpellier, France, 2018. (acceptance rate: 69/346 = 19.9%)
5. Sen Chen, Guozhu Meng, Ting Su, Lingling Fan, Minhui Xue, Yinxing Xue, Yang Liu, and Lihua Xu, "AUSERA: Large-Scale Automated Security Risk Assessment of Global Mobile Banking Apps".
(1) Until now, 21 banking entities have confirmed 126 weaknesses, 52 weaknesses have been patched. Our work has also received positive feedback and improved their security policies.
(2) won Research Tool Award in NASAC 2018 (National Software Application Conference) held by CCF.
4. Lingling Fan, Ting Su, Sen Chen, Guozhu Meng, Yang Liu, Lihua Xu, Geguang Pu and Zhendong Su, "Large-Scale Analysis of Framework-Specific Exceptions in Android Apps", In Proceedings of the 40th International Conference on Software Engineering (ICSE2018), Gothenburg, Sweden, 2018. (acceptance rate: 105/502 = 20.9%)
(1) dataset of Android exceptions statistics of dataset access award press (2) ACM SIGSOFT Distinguished Paper Award
3. Sen Chen, Minhui Xue, Lingling Fan, Shuang Hao, Lihua Xu, Haojin Zhu, and Bo Li, "Automated Poisoning Attacks and Defenses in Malware Detection System: An Adversarial Machine Learning Approach", In Proceedings of the Elsevier Computers & Security, 2017. (accepted)
Highlight: dataset of Android malware
2. Lingling Fan, Sen Chen, Lihua Xu, Zongyuan Yang, Huibiao Zhu, Model-Based Continuous Verification, In Proceedings of the IEEE ASIA-Pacific Software Engineering Conference (APSEC2016), Hamilton, New Zealand, 2016. (acceptance rate: 19.7%)
1. Sen Chen, Minhui Xue, Zhushou Tang, Lihua Xu, and Haojin Zhu, "StormDroid: A Streaminglized Machine Learning-Based System for Detecting Android Malware", In Proceedings of the ACM Asia Conference on Computer and Communications Security (AsiaCCS2016), Xi'an, China, 2016. (acceptance rate: 20.9%)
Highlight: statistics of dataset access
Sen Chen, Minhui Xue, Lihua Xu, "Poster: Towards Adversarial Detection of Mobile Malware", In Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom2016), New York, America, 2016.
Lingling Fan, Minhui Xue, Sen Chen, Lihua Xu, Haojin Zhu, "POSTER: Accuracy vs. Time Cost: Detecting Android Malware through Pareto Ensemble Pruning", In Proceedings of the ACM Conference on Computer and Communications Security (CCS2016), Vienna, Austria, 2016.
NASAC 2018 Research Tool Award, CCF, November 2018
FSE 2018 Travel Grant Award, ACM/SIGSOFT, September 2018
ACM SIGSOFT Distinguished Paper Award, May, 2018
University-level Outstanding Student, ECNU, China, December 2017
Graduate Student Visiting Scholarship, Singapore, February 2017
MobiCom 2016 Travel Grant Award, ACM/SIGMOBILE, August 2016
Graduate Student Overseas Visiting Scholarship, China, June 2016
University-level Outstanding Student, ECNU, China, December 2015
First Class Excellent Undergraduate Scholarship, October 2013
University-level Outstanding Student, October, 2013
National Scholarship, The Ministry of Education, China, October 2012
AUSERA: An Automated Tool for Security Risk Assessment of
Mobile Banking Apps,
NASAC'18, Shenzhen, China, November 2018
Are Mobile Banking Apps Secure? What Can Be Improved?
FSE'18, FL, USA, November 2018
Automated Semantic-risk Assessment for Financial Apps,
OCBC Bank, Singapore, January 2018
VulDigger: A Just-in-Time and Cost-Aware Tool for Digging Vulnerability-Contributing Changes,
GLOBECOM'17, Singapore, December 2017
A Semantic-Based Analysis of Android Malware for Detection,
SICW GovWare, Singapore, September 2017
Towards Adversarial Detection of Mobile Malware,
MobiCom'16, New York, USA, October 2016
Machine Learning-Based Approach for Android Malware Detection,
Nanyang Technological University, Singapore, October 2016
A Streaminglized Machine Learning-Based System for Detecting Android Malware,
AsiaCCS'16, Xi'an, China, June 2016
Android Engineer -SOHU, Beijing, China - February to September, 2014
-Android application development and Android UI analysis
JAVA Engineer -UFIDA, Beijing, China - March to July, 2013
-Software application development and testing
ISSTA 2018, ASE 2018, USENIX Security 2018, CCS 2018, Oakland 2018,2019