About our lab: SQSLab, short for Software Quality and Security, is interested in working with emerging problems in the era of cloud computing and big data analysis. Particular topics include Software Engineering, Performance Guarantee in Cloud Computing, Mobile Security and Energy Saving on Mobile Devices.
For model checking and testing, we've developed a model-Based continuous verification system called Eunomia, which bi-directionally checks the consistency of model and source code.
For mobile security, we are working on malware detection and classification (refer to AsiaCCS 2016). We've developed KuafuDet (refer to MobiCom 2016) for android malware detection using machine learning in adversarial environment. We proposed a malware detection system, termed Begonia (refer to CCS 2016), through Pareto ensemble learning to trade off classification accuracy and time cost.
We are still looking for new talented Masters and Ph.D. students.
Please send me email(firstname.lastname@example.org).
Fei Xu, Fangming Liu, Hai Jin, Heterogeneity and Interference-Aware Virtual Machine Provisioning for Predictable Performance in the Cloud, IEEE Transactions on Computers, 2016.
Lingling Fan, Sen Chen, Lihua Xu, Zongyuan Yang, Huibiao Zhu, Model-Based Continuous Verification, IEEE ASIA-Pacific Software Engineering Conference (APSEC), 2016. (Acceptance Rate: 43/218 = 19.7%)
Congratulations to Sen Chen, who has received the MobiCom 2016 Travel Grant Award, ACM/SIGMOBILE, August, 2016.
Lingling fan, Minhui Xue, Sen Chen, Lihua Xu, Haojin Zhu, POSTER: Accuracy vs. Time Cost: Detecting Android Malware through Pareto Ensemble, ACM CCS'16, 2016.
Sen Chen, Minhui Xue, Lihua Xu, Poster: Towards Adversarial Detection of Mobile Malware, ACM MobiCom'16, 2016.
Sen Chen, Minhui Xue, Zhushou Tang, Lihua Xu, Haojin Zhu, StormDroid: A Streaminglized Machine Learning-based System for Detecting Android Malware, ACM ASIACCS'16, 2016