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.
Congratulations to Yunbo Tao. The paper "3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack" is accepted by ICCV 2023!.
Congratulations to Dr. Xie to be selected as the most excellent young faculty (only ten in university every year)(华中科技大学十佳青年教工).
Congratulations to Dr. Xie to get the National Key R&D Program of China under grant No.2022YFB4501300 （国家重点研发计划青年科学家项目）
Congratulations to Liangkang Zhang. The paper "A Novel Hybrid Model for Docker Container Workload Prediction" is accepted to be published in IEEE Transactions on Network and Service Management (TNSM).
Congratulations to Die Hu. The paper "Internet Public Safety Event Grading and Hybrid Storage based on Multi-Feature Fusion for Social Media Texts" is accepted to be published in Database Systems for Advanced Applications (DASFAA).
Congratulations to Die Hu. The paper "EGC: A novel event-oriented graph clustering framework for social media text" is accepted to be published in Information Processing & Management (IPM).
Congratulations to Yafeng Wu. The paper "Paradise: Real-time, Generalized, and Distributed Provenance-Based Intrusion Detection" is accepted to be published in IEEE Transactions on Dependable and Secure Computing (TDSC).
Dr. Xie is invited to be a Program Committee member of the Application of Intelligent Technology in Security (AITS 2021)
Dr. Xie is invited to be a Program Committee member of the USENIX workshop on Theory and Practice of Provenance (TaPP 2021)
Dr. Xie has been selected in the 2020 WuHan Yellow Crane Talents (黄鹤英才) Program
Congratulations to Dr. Xie to get the Fundamental Research Funds CCF-KunPeng Research Initiative. The project "Research on Efficient Storage and Accurate Detection of Large-Scale Dynamic Provenance Stream for Intrusion Behavior" will be supported during 2020-2021
The paper "Real-time Prediction of Docker Container Resource Load Based on A Hybrid Model of ARIMA and Triple Exponential Smoothing" is accepted to be published in IEEE Transactions on Cloud Computing (TCC).
Dr. Xie is invited to be a Program Committee member of the Massive Storage Systems and Technology (MSST 2020, CCF B) May 4—8, 2020, in Santa Clara, CA
Dr. Xie is invited to be a Program Committee member of the workshop on Big Data and Machine Learning for Security (BDMLS), 2020
The paper "P-Gaussian: Provenance based Gaussian Distribution for Detecting Intrusion Behavior Variants Using High Efficient and Real-time Memory Databases" is accepted to be published in IEEE Transactions on Dependable and Secure Computing (TDSC).
Dr. Xie is invited to be a Program Committee member of the 12th International Workshop on Theory and Practice of Provenance (in cooperation with USENIX) (TaPP 2020)
Congratulations to Dr. Xie to get the National Science Foundation of China (国家自然科学基金面上项目) under grant no. 61972449! The project "Efficient Storage and Accurate Detection of Big Data for Intrusion Behavior" will be supported from 2020.01 to 2023.12
The paper "A Docker Container Anomaly Monitoring System Based on Optimized Isolation Forest" is accepted by IEEE Transactions on Cloud Computing(TCC), congratulations to Zhuping Zou, Kai Huang and Gongming Xu!
Dr. Xie is invited to be a session chair in 14th IEEE International Conference on Network, Architecture and Storage(NAS),on August 15-17, 2019, in Enshi, China
Congratulations to Dr. Xie to get the Fundamental Research Funds for the Central Universities under Grant No. 2019kfyXKJC021. The project "Public security related big data storage and analysis" will be supported during 2019-2021
The paper "Efficient provenance management via clustering and hybrid storage in big data environments" is accepted by IEEE Transactions on Big Data (TBD), congratulations to Die Hu, Gongming Xu and Xinrui Gu!
Dr. Xie is invited to be a PC member of the workshop on Big Data and Machine Learning for Security (BDMLS), 2019
Dr. Xie is honored to attend the award ceremony at the China Computer Congress on Oct. 26, 2018
The paper "Pagoda: A hybrid approach to enable efficient and real-time provenance based intrusion detection in big data environments" is accepted to be published in IEEE Transactions on Dependable and Secure Computing (TDSC).
Dr. Xie has got a grant from CCF and NSFOCUS to do the research on container security
Yunbo Tao, Daizong Liu, Pan Zhou, Yulai Xie*, Wei Du, Wei Hu*. 3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack.ICCV, 2023..
Liangkang Zhang, Yulai Xie, Minpeng Jin, Pan Zhou, Gongming Xu, Yafeng Wu, Dan Feng, Darrell Long. A Novel Hybrid Model for Docker Container Workload Prediction, IEEE Transactions on Network and Service Management, 2023 (Accepted).
Die Hu, Yulai Xie, Dan Feng, Shixun Zhao, Pengyu Fu. Internet Public Safety Event Grading and Hybrid Storage based on Multi-Feature Fusion for Social Media Texts. The 28th International Conference on Database Systems for Advanced Applications (DASFAA) 2023, (CCF B).
Die Hu, Dan Feng, Yulai Xie. EGC: A novel event-oriented graph clustering framework for social media text. Information Processing & Management, (IPM 22), vol 59(6), pages: 103059, 2022. (CCF B, SCI1区).
Yafeng Wu, Yulai Xie*, Xuelong Liao, Pan Zhou, Dan Feng, Lin Wu, Avani Wildani, Darrell Long, Paradise: Real-time, Generalized, and Distributed Provenance-Based Intrusion Detection, IEEE Transactions on Dependable and Secure Computing, 2022 (CCF A, Accepted, Corresponding Author).
Yulai Xie, Shuai Tong, Pan Zhou, Yuli Li, Dan Feng, Efficient Storage Management for Social Network Events Based on Clustering and Hot/Cold Data Classification, IEEE Transactions on Computational Social Systems. 2022 (Accepted.)
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.
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)
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)
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)
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).
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).
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).
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).
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](CCF A)
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.
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)
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.
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)
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)
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)
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.
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.
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.
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.
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.
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.
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.
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.
Cloud Edge Converged Secure Storage System，the National Key R&D Program of China under grant No.2022YFB4501300 (国家重点研发青年科学家项目), PI (2023.3-2026.2)
Research on Efficient Storage and Accurate Detection of Large-Scale Dynamic Provenance Stream for Intrusion Behavior, CCF-KunPeng Research Initiative (CCF-绿盟科技鲲鹏科研计划), PI 2020-2021
Efficient Storage and Accurate Detection of Big Data for Intrusion Behavior, National Science Foundation of China (国家自然科学基金面上项目), No. 61972449, PI, 2020.1-2023.12
Public security related big data storage and analysis,the Fundamental Research Funds for the Central Universities under Grant No. 2019kfyXKJC021, PI, 2019-2021
Container-based anomaly detection technology based on improved Isolation Forest,CCF-KunPeng Research Initiative（CCF-绿盟科技鲲鹏科研计划）, PI, 2018-2019
Provenance-based network security, Wuhan Application Basic Research Program （武汉市应用基础研究计划）, PI, 2017-2019.
Efficient Storage and Query for Large-Scale Provenance in Distributed Environment, Hubei Natural Science Foundation（湖北省自然科学基金）, PI,2017-2018.
System and Method for Provenance-based Intrusion Detection,CCF-Venustech Hongyan Research Initiative （CCF-启明星辰鸿雁计划）, PI, 2016-2017
Innovation Research Foundation of Huazhong University of Science and Technology, PI, 2016-2018
Efficient and Intelligent Provenance-based Intrusion Detection and Data Recovery, National Science Foundation of China（国家自然科学基金青年项目）,PI, No.61402189, 2015.1-2017.12.
Efficient Storage and Management for Large-scale Provenance, Postdoctoral Foundation of China （中国博士后面上项目）, PI, 2014-2015.
Yue Huang
Kai Huang
Xinrui Gu
Xuelong Liao
Yuli Li
Xiang Xia
Zhuping Zhou
Pengyu Fu
Die Hu
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 in 2016 China Computer Congress
Awards in 2018 China Computer Congress
Provenance-based network security, National information storage conference, Sep. 2017
Oasis: an active storage framework for object storage platform, CUHK seminar, Dec. 2015
A Hybrid Approach for Efficient Provenance Storage, CIKM 2012.
Design and Evaluation of Oasis: An Active Storage Framework based on T10 OSD Standard, MSST 2011.
Compressing Provenance Graphs, The 9th Annual SSRC Research Retreat, May 2011.
Provenance Pruning for Local Storage Systems Based on the PASS Model, SSRC Lab Seminar, March 2011.
Application of Intelligent Technology in Security (AITS2021, PC member)
USENIX workshop on Theory and Practice of Provenance (TaPP2021, PC member)
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).
Dr. Avani Wildani is an Assistant Professor in Mathematics/Computer Science and Neuroscience at Emory University. Prior to that, she was a Pioneer Postdoctoral Fellow in computational neuroscience at the Salk Institute for Biological Sciences. She has worked as a systems administrator, video game tester, and lab animal wrangler as well as research internships at Google, IBM Almaden, and Sandia National Laboratories. She earned her B.S. in Computer Science and Mathematics at Harvey Mudd College and her Ph.D. in Computer Science at UC Santa Cruz under Dr. Ethan Miller. Her interests are centered around information storage and retrieval across different storage models, with application domains including access prediction, data deduplication, archival economics, power management, wireless mesh networks, auditory receptive field characterization, and pollution monitoring.
She is the co-PI of the SimBioSys lab at Emory, and her group focuses on information models in cloud and communication systems, particularly those with biological connections, with a long term goal of categorizing neural information. She was co-chair of the inaugural computer systems track at the 2016 Grace Hopper Celebration of Women in Computing.
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)
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]
[click to download Dataset-TOS13]
[click to download Dataset-Camflow]