Yulai Xie, PhD Advisor, Associate Professor
School of Cyber Science and Engineering, Huazhong University of Science and Technology
School of Computer Science, Huazhong University of Science and Technology
F311,Wuhan National Lab for Optoelectronics, Wuhan, China, 430074
ylxie@hust.edu.cn


Yulai Xie received his Ph.D. degree working with Prof. Dan Feng in Computer Architecture from Huazhong University of Science and Technology (HUST) in 2013. He has been a visiting scholar in University of California, Santa Cruz working with Prof.Darrell Long during 2010-2011 and a visiting researcher in Chinese University of Hong Kong in 2015. He is currently leading the provenance-aware storage and security research group in Data Storage and Application Lab, HUST. His current research interests include provenance-aware data storage and security, intrusion detection, digital forensic, social computing, distributed file systems, cloud storage and cloud security. He currently serves as the web editor of IEEE Letters of the Computer Society. He is also an editoral board member of GSL Journal of Forensic Research and International Journal of Next-Generation Networks. He has been selected in the WuHan Yellow Crane Talents Program. He has been selected in the CCF-venusterch program and CCF-NSFOCUS program. He is a member of ACM and IEEE, and a senior member of China Computer Federation (CCF).

Always looking for self-motivated students with strong background in computer security, architecture and system. If you have strong skills in mathematics, you are also welcome to join in my group. Please do not hesitate to contact me (ylxie@hust.edu.cn) if you are interested to do the research on storage or security.

News

Publications

  1. Pan Zhou, Shimin Gong, Zichuan Xu, Lixing Chen, Yulai Xie, Changkun Jiang, Xiaofeng Ding, Trustworthy and Context-Aware Distributed Online Learning for Autoscaling Content Caching in Collaborative Mobile Edge Computing, IEEE Transactions on Cognitive Communications and Networking, Accepted.

  2. Pan Zhou, Yulai Xie, Ben Niu, Lingjun Pu, Zichuan Xu, Hao Jiang, Huawei Huang,QoE-Aware 3D Video Streaming via Deep Reinforcement Learning in Software Defined Networking Enabled Mobile Edge Computing, IEEE Transactions on Network Science and Engineering, 2021,Page(s):419 – 433 (SCI, IF=5.213)

  3. Zeyue Xue, Pan Zhou, Zichuan Xu, Xiumin Wang, Yulai Xie, Xiaofeng Ding, Shiping Wen,A Resource-Constrained and Privacy-Preserving Edge Computing Enabled Clinical Decision System: A Federated Reinforcement Learning Approach,IEEE internet of things Journal, DOI: 10.1109/JIOT.2021.3057653 (SCI, IF=9.936)

  4. Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou, Yu Cheng, Wei Wei, Zichuan Xu, Yulai Xie. Context-aware Biaffine Localizing Network for Temporal Sentence Grounding. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.(CCF A)

  5. Yulai Xie, Minpeng Jin, Zhuping Zou, Gongming Xu, Dan Feng, Wenmao Liu, Darrell Long, Real-time Prediction of Docker Container Resource Load Based on A Hybrid Model of ARIMA and Triple Exponential Smoothing, IEEE Transactions on Cloud Computing, April 2020, Accepted (IF=5.967).

  6. Yulai Xie, Yafeng Wu, Dan Feng, Darrell Long, P-Gaussian: Provenance-Based Gaussian Distribution for Detecting Intrusion Behavior Variants Using High Efficient and Real Time Memory Databases, IEEE Transactions on Dependable and Secure Computing, December 2019, Accepted [pass-dataset-P-Gaussian] [SPADE-dataset-P-Gaussian] [Camflow-dataset-P-Gaussian] (CCF A,IF=6.404).

  7. Zhuping Zou, Yulai Xie*, Kai Huang, Gongming Xu, Dan Feng, Darrell Long, A Docker Container Anomaly Monitoring System Based on Optimized Isolation Forest, IEEE Transactions on Cloud Computing, August 2019 (Corresponding Author, IF=5.967).

  8. Die Hu, Dan Feng, Yulai Xie*,Gongming Xu, Xinrui Gu, Darrell Long, Efficient Provenance Management via Clustering and Hybrid Storage in Big Data Environments, IEEE Transactions on Big Data, March 2019 (Corresponding Author).

  9. Yulai Xie, Dan Feng, Yuchong Hu, Yan Li, Staunton Sample, Darrell Long, Pagoda: A Hybrid Approach to Enable Efficient Real-time Provenance Based Intrusion Detection in Big Data Environments, IEEE Transactions on Dependable and Secure Computing, August 2018. [Source Code] [pass-dataset-Pagoda] (CCF A,IF=6.404)

  10. Yulai Xie, Kiran-Kumar Muniswamy-Reddy, Dan Feng, Yan Li, Darrell D. E. Long, Evaluation of a Hybrid Approach for Efficient Provenance Storage,ACM Transactions on Storage, vol. 9, no. 4, November 2013. [Source Code](CCF A)

  11. Yulai Xie, Dan Feng, Zhipeng Tan, Junzhe Zhou, Design and Evaluation of a Provenance-based Rebuild Framework,IEEE Transactions on Magnetics, vol. 49, no. 6, June 2013.

  12. Yulai Xie, Dan Feng, Xuelong Liao, Leihua Qin, Efficient monitoring and forensic analysis via accurate network-attached provenance collection with minimal storage overhead, Digital Investigation, September 2018, Vol.26, pp:19-28. (IF=1.774)

  13. Xuelong Liao, Yulai Xie, Zhen Rong, Leihua Qin, Jianxi Chen, Dan Feng, Research on provenance collection and storage based on object-based storage system. Journal of Frontiers of Computer Science and Technology, 2018, 12(2):218-230.

  14. Kai Huang, Qinglong Meng, Yulai Xie, Leihua Qin, Dan Feng, Dynamic weighted scheduling strategy based on Docker swarm cluster. Journal of Computer Applications, 2018, 38(5)

  15. Yulai Xie, Dan Feng, Zhipeng Tan, Junzhe Zhou, Unifying Intrusion Detection and Forensic Analysis via Provenance Awareness,Future Generation Computer Systems, 61(2016):26-36.(IF=4.639)

  16. Yulai Xie, Dan Feng, Yan Li, Darrell D. E. Long, Oasis: an Active Storage Framework for Object Storage Platform ,Future Generation Computer Systems, vol. 56, March 2016, Pages 746-758.(IF=4.639)

  17. Yulai Xie, Kiran-Kumar Muniswamy-Reddy, Dan Feng, Yan Li, Darrell D. E. Long, Zhipeng Tan, Lei Chen, A Hybrid Approach for Efficient Provenance Storage,The 21st ACM International Conference on Information and Knowledge Management (CIKM), October 2012.

  18. Yulai Xie, Dan Feng, Zhipeng Tan, Lei Chen, Junzhe Zhou, “Experiences Building a Provenance-based Reconstruction System,” The Storage System, Hard Disk and Solid State Technologies Summit in conjunction with the APMRC conference, October 2012.

  19. Yong Wan, Dan Feng, Fang Wang, Liang Ming, Yulai Xie, An In-depth Analysis of TCP and RDMA Performance on Modern Server Platform,The 7th IEEE International Conference on Networking, Architecture, and Storage, June 2012.

  20. Zhipeng Tan, Yanli Yuan, Tian Zan, Yulai Xie, MO_AOBS: Researches of Method Object in Active Object-based Storage Systems,2011 International Conference on Computer Science and Network Technology, December 2011.

  21. Yulai Xie, Kiran-Kumar Muniswamy-Reddy, Darrell D. E. Long, Ahmed Amer, Dan Feng, Zhipeng Tan, Compressing Provenance Graphs,The 3rd USENIX Workshop on the Theory and Practice of Provenance, June 2011.

  22. Yulai Xie, Kiran-Kumar Muniswamy-Reddy, Dan Feng, Darrell D. E. Long, Yangwook Kang, Zhongying Niu, Zhipeng Tan, Design and Evaluation of Oasis: An Active Storage Framework based on T10 OSD Standard,The 27th International Symposium on Massive Storage Systems and Technologies (MSST), May 2011.

  23. Zhipeng Tan, Yulai Xie, Quanli Gui, Tian Zhang, Wenhua Zhang, I/O Response Rate Analysis in the Replicate-Based Object Storage System,The 9th International Symposium on Distributed Computing and Applications To Business, Engineering & Science, August 2010.

  24. Yulai Xie, Dan Feng, Fang Wang, Research on Active Storage and Its Implementation on Object Storage,Communications of China Computer Federation, vol. 4, no.11, November 2008.

Grants

Research Group

Yue Huang

Kai Huang

Xinrui Gu

Xuelong Liao

Yuli Li

Xiang Xia

Zhuping Zhou

Pengyu Fu

Die Hu

Activities

National information storage conference in Xi'an, in Sep. 2017

China Computer Congress in Fuzhou, in Oct. 2017

Yafeng Wu in RAID conference in Beijing, in Sep. 2019

China Computer Congress in Suzhou, in Oct. 2019

Awards & Certifications

Awards in 2016 China Computer Congress

Awards in 2018 China Computer Congress

Talks

PC Member and Reviewer

Teaching

Associate

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Darrell D. E. Long

Darrell D. E. Long is the Director of the Storage Systems Research Center. He is Professor of Computer Engineering and holds the Kumar Malavalli Endowed Chair. His current research interests in the storage systems area include high performance storage systems, archival storage systems and energy-efficient storage systems. His research also includes computer system reliability, video-on-demand, applied machine learning, mobile computing and cyber security.

Dr. Long is Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and Fellow of the American Association for the Advancement of Science (AAAS). He is Visiting Professor at the United States Naval Postgraduate School and Professor ad Honorem de la Universidad Catolica del Uruguay. He has been Visiting Professor at the University of Technology, Sydney, and Professeur Invite at the Universite Paris-Dauphine, the Conservatoire National des Arts et Metiers and the Universite Paris-Descartes. He is an Associate Member, European Organization for Nuclear Research (CERN).

His homepage can be found here.

Datasets

Yulai Xie, Yafeng Wu, Dan Feng, Darrell Long, P-Gaussian: Provenance-Based Gaussian Distribution for Detecting Intrusion Behavior Variants Using High Efficient and Real Time Memory Databases, IEEE Transactions on Dependable and Secure Computing, December 2019, Accepted [pass-dataset-P-Gaussian] [SPADE-dataset-P-Gaussian] [Camflow-dataset-P-Gaussian] (CCF A,IF=6.404).

Yulai Xie, Dan Feng, Yuchong Hu, Yan Li, Staunton Sample, Darrell Long, Pagoda: A Hybrid Approach to Enable Efficient Real-time Provenance Based Intrusion Detection in Big Data Environments, IEEE Transactions on Dependable and Secure Computing, August 2018. [Source Code] [pass-dataset-Pagoda] (CCF A,IF=6.404)

Yulai Xie, Kiran-Kumar Muniswamy-Reddy, Dan Feng, Yan Li, Darrell D. E. Long, Evaluation of a Hybrid Approach for Efficient Provenance Storage,ACM Transactions on Storage, vol. 9, no. 4, November 2013. [Source Code] [Dataset-TOS13] (CCF A)

Dataset-pass

The Harvard PASS system (https://syrah.eecs.harvard.edu/pass) intercepts system calls to generate provenance information. When an event occurs, the corresponding system call is triggered. This dataset distinguishes the same processes by version, ensuring that no loops occur. In addition to dependency properties (such as INPUT, GENERATEDBY, FORKPARENT, RECV, and SEND) between objects, the object's own properties (such as NAME, ENV, ARGV, PID, EXECTIME, and TYPE) are also collected.

There are mainly three types of objects collected: file object, process object and network connection object. For file object, its attributes include specific information about the file itself, such as file name, file storage space, storage location, and file node number. For process object, its attributes mainly contain the process name, the PID number of the process, environment variables, the process creation time, etc. For network connection object, it is used to record the transmission of data on the network. Network connection objects can be considered as file objects, the attributes of which include the source port, the destination port, the source IP address, and the destination IP address. There are mainly three types of dependencies collected: (1) FORKPARENT: Process to Process, if a process P creates process Q, the provenance record "Q FORKPARENT P" is generated, we represent it as, Q->P. (2) INPUT: Process to File, if a process P reads the contents of a file A, the provenance record "P INPUT A" is generated, we represent it as, P->A. (3) GENERATEDBY: Network connection object to Process, if a process P sends data from the network connection object B, the provenance record "B GENERATEDBY P" is generated, we represent it as, B->P.

[click to download Dataset-PASS]

Dataset-TOS13

[click to download Dataset-TOS13]

Dataset-Camflow

[click to download Dataset-Camflow]

Dataset-Spade
[click to download Dataset-SPADE]