Publications
2012
F. P. An, J. Z. Bai, A. B. Balantekin, et al., “Observation of electron-antineutrino disappearance at Daya Bay”, March 8, 2012,
The Daya Bay Reactor Neutrino Experiment has measured a non-zero value for the neutrino mixing angle θ13 with a significance of 5.2 standard deviations. Antineutrinos from six 2.9 GWth reactors were detected in six antineutrino detectors deployed in two near (flux-weighted baseline 470 m and 576 m) and one far (1648 m) underground experimental halls. With 55 days of data, 10416 (80376) electron antineutrino candidates were detected at the far hall (near halls). The ratio of the observed to expected number of antineutrinos at the far hall is R=0.940 ±0.011( stat) ± 0.004( syst). A rate-only analysis finds sin2 2 θ13 - 0.092 ± 0.016( stat}) ± 0.005(syst) in a three-neutrino framework.
Full Author list: F. P. An, J. Z. Bai, A. B. Balantekin, H. R. Band, D. Beavis, W. Beriguete, M. Bishai, S. Blyth, R. L. Brown, G. F. Cao, J. Cao, R. Carr, W. T. Chan, J. F. Chang, Y. Chang, C. Chasman, H. S. Chen, H. Y. Chen, S. J. Chen, S. M. Chen, X. C. Chen, X. H. Chen, X. S. Chen, Y. Chen, Y. X. Chen, J. J. Cherwinka, M. C. Chu, J. P. Cummings, Z. Y. Deng, Y. Y. Ding, M. V. Diwan, L. Dong, E. Draeger, X. F. Du, D. A. Dwyer, W. R. Edwards, S. R. Ely, S. D. Fang, J. Y. Fu, Z. W. Fu, L. Q. Ge, V. Ghazikhanian, R. L. Gill, J. Goett, M. Gonchar, G. H. Gong, H. Gong, Y. A. Gornushkin, L. S. Greenler, W. Q. Gu, M. Y. Guan, X. H. Guo, R. W. Hackenburg, R. L. Hahn, S. Hans, M. He, Q. He, W. S. He, K. M. Heeger, Y. K. Heng, P. Hinrichs, T. H. Ho, Y. K. Hor, Y. B. Hsiung, B. Z. Hu, T. Hu, T. Hu, H. X. Huang, H. Z. Huang, P. W. Huang, X. Huang, X. T. Huang, P. Huber, Z. Isvan, D. E. Jaffe, S. Jetter, X. L. Ji, X. P. Ji, H. J. Jiang, W. Q. Jiang, J. B. Jiao, R. A. Johnson, L. Kang, S. H. Kettell, M. Kramer, K. K. Kwan, M. W. Kwok, T. Kwok, C. Y. Lai, W. C. Lai, W. H. Lai, K. Lau, L. Lebanowski, J. Lee, M. K. P. Lee, R. Leitner, J. K. C. Leung, K. Y. Leung, C. A. Lewis, B. Li, F. Li, G. S. Li, J. Li, Q. J. Li, S. F. Li, W. D. Li, X. B. Li, X. N. Li, X. Q. Li, Y. Li, Z. B. Li, H. Liang, J. Liang, C. J. Lin, G. L. Lin, S. K. Lin, S. X. Lin, Y. C. Lin, J. J. Ling, J. M. Link, L. Littenberg, B. R. Littlejohn, B. J. Liu, C. Liu, D. W. Liu, H. Liu, J. C. Liu, J. L. Liu, S. Liu, X. Liu, Y. B. Liu, C. Lu, H. Q. Lu, A. Luk, K. B. Luk, T. Luo, X. L. Luo, L. H. Ma, Q. M. Ma, X. B. Ma, X. Y. Ma, Y. Q. Ma, B. Mayes, K. T. McDonald, M. C. McFarlane, R. D. McKeown, Y. Meng, D. Mohapatra, J. E. Morgan, Y. Nakajima, J. Napolitano, D. Naumov, I. Nemchenok, C. Newsom, H. Y. Ngai, W. K. Ngai, Y. B. Nie, Z. Ning, J. P. Ochoa-Ricoux, A. Olshevski, A. Pagac, S. Patton, C. Pearson, V. Pec, J. C. Peng, L. E. Piilonen, L. Pinsky, C. S. J. Pun, F. Z. Qi, M. Qi, X. Qian, N. Raper, R. Rosero, B. Roskovec, X. C. Ruan, B. Seilhan, B. B. Shao, K. Shih, H. Steiner, P. Stoler, G. X. Sun, J. L. Sun, Y. H. Tam, H. K. Tanaka, X. Tang, H. Themann, Y. Torun, S. Trentalange, O. Tsai, K. V. Tsang, R. H. M. Tsang, C. Tull, B. Viren, S. Virostek, V. Vorobel, C. H. Wang, L. S. Wang, L. Y. Wang, L. Z. Wang, M. Wang, N. Y. Wang, R. G. Wang, T. Wang, W. Wang, X. Wang, X. Wang, Y. F. Wang, Z.Wang, Z.Wang, Z. M.Wang, D. M.Webber, Y. D.Wei, L. J.Wen, D. L.Wenman, K. Whisnant, C. G. White, L. Whitehead, C. A. Whitten Jr., J. Wilhelmi, T. Wise, H. C. Wong, H. L. H. Wong, J. Wong, E. T. Worcester, F. F. Wu, Q. Wu, D. M. Xia, S. T. Xiang, Q. Xiao, Z. Z. Xing, G. Xu, J. Xu, J. Xu, J. L. Xu, W. Xu, Y. Xu, T. Xue, C. G. Yang, L. Yang, M. Ye, M. Yeh, Y. S. Yeh, K. Yip, B. L. Young, Z. Y. Yu, L. Zhan, C. Zhang, F. H. Zhang, J. W. Zhang, Q. M. Zhang, K. Zhang, Q. X. Zhang, S. H. Zhang, Y. C. Zhang, Y. H. Zhang, Y. X. Zhang, Z. J. Zhang, Z. P. Zhang, Z. Y. Zhang, J. Zhao, Q. W. Zhao, Y. B. Zhao, L. Zheng, W. L. Zhong, L. Zhou, Z. Y. Zhou, H. L. Zhuang, J. H. Zou
Scott Campbell, Jason Lee, “Prototyping a 100G Monitoring System”, 20th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2012), February 12, 2012,
The finalization of the 100 Gbps Ethernet Specification has been a tremendous increase in these rates arriving into data centers creating the need to perform security monitoring at 100 Gbps no longer simply an academic exercise. We show that by leveraging the ‘heavy tail flow effect’ on the IDS infrastructure, it is possible to perform security analysis at such speeds within the HPC environment. Additionally, we examine the nature of current traffic characteristics, how to scale an IDS infrastructure to 100Gbps.
2011
Scott Campbell, Jason Lee, “Intrusion Detection at 100G”, The International Conference for High Performance Computing, Networking, Storage, and Analysis, November 14, 2011,
Driven by the growing data transfer needs of the scientific community and the standardization of the 100 Gbps Ethernet Specification, 100 Gbps is now becoming a reality for many HPC sites. This tenfold increase in bandwidth creates a number of significant technical challenges. We show that by using the heavy tail flow effect as a filter, it should be possible to perform active IDS analysis at this traffic rate using a cluster of commodity systems driven by a dedicated load balancing mechanism. Additionally, we examine the nature of current network traffic characteristics applying them to 100Gpbs speeds
Scott Campbell, Steve Chan and Jason Lee, “Detection of Fast Flux Service Networks”, Australasian Information Security Conference 2011, January 17, 2011,
Fast Flux Service Networks (FFSN) utilize high availability server techniques for malware distribution. FFSNs are similar to commercial content distribution networks (CDN), such as Akamai, in terms of size, scope, and business model, serving as an outsourced content delivery service for clients. Using an analysis of DNS traffic, we derive a sequential hypothesis testing algorithm based entirely on traffic characteristics and dynamic white listing to provide real time detection of FFDNs in live traffic. We improve on existing work, providing faster and more accurate detection of FFSNs. We also identify a category of hosts not addressed in previous detectors - Open Content Distribution Networks (OCDN) that share many of the characteristics of FFSNs
2010
Sim A., Gunter D., Natarajan V., Shoshani A., Williams D., Long J., Hick J., Lee J., Dart E., “Efficient Bulk Data Replication for the Earth System Grid”, Data Driven E-science: Use Cases and Successful Applications of Distributed Computing Infrastructures (Isgc 2010), Springer-Verlag New York Inc, 2010, 435,
Kettimuthu Raj, Sim Alex, Gunter Dan, Allcock Bill, Bremer Peer T., Bresnahan John, Cherry Andrew, Childers Lisa, Dart Eli, Foster Ian, Harms Kevin, Hick Jason, Lee Jason, Link Michael, Long Jeff, Miller Keith, Natarajan Vijaya, Pascucci Valerio, Raffenetti Ken, Ressman David, Williams Dean, Wilson Loren, Winkler Linda, “Lessons Learned from Moving Earth System Grid Data Sets over a 20 Gbps Wide-Area Network”, Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing HPDC 10, New York NY USA, 2010, 316--319,
A. Sim, D. Gunter, V. Natarajan, A. Shoshani, D. Williams, J. Long, J. Hick, J. Lee, E. Dart, “Efficient Bulk Data Replication for the Earth System Grid”, International Symposium on Grid Computing, 2010,
2007
Chin Guok, Jason R Lee, Karlo Berket, “Improving the Bulk Data Transfer Experience”, Management of IP Networks and Services Special Issue, January 1, 2007,
Scientific computations and collaborations increasingly rely on the network to provide high-speed data transfer, dissemination of results, access to instruments, support for computational steering, etc. The Energy Sciences Network is establishing a science data network that is logically separate from the production IP core network. One of the requirements of the science data network is the ability to provide user driven bandwidth allocation. In a shared network environment, some reservations may not be granted due to the lack of available bandwidth on any single path. In many cases, the available bandwidth across multiple paths would be sufficient to grant the reservation. In this paper we investigate how to utilize the available bandwidth across multiple paths in the case of bulk data transfer.
Matthias Vallentin, Robin Sommer, Jason Lee, Craig Leres, Vern Paxson, Brian Tierney,, “The NIDS Cluster: Scalable, Stateful Network Intrusion Detection on Commodity Hardware”, Proceedings of the Symposium on Recent Advances in Intrusion Detection, Queensland, Australia,, January 1, 2007,
2006
E. Wes Bethel, Scott Campbell, Eli Dart, Jason Lee, Steven A. Smith, Kurt Stockinger, Brian Tierney, Kesheng Wu, “Interactive Analysis of Large Network Data Collections Using Query-Driven Visualization”, DOE Report, September 26, 2006, LBNL 59166
Realizing operational analytics solutions where large and complex data must be analyzed in a time-critical fashion entails integrating many different types of technology. Considering the extreme scale of contemporary datasets, one significant challenge is to reduce the duty cycle in the analytics discourse process. This paper focuses on an interdisciplinary combination of scientific data management and visualization/analysistechnologies targeted at reducing the duty cycle in hypothesis testing and knowledge discovery. We present an application of such a combination in the problem domain of network traffic dataanalysis. Our performance experiment results, including both serial and parallel scalability tests, show that the combination can dramatically decrease the analytics duty cycle for this particular application. The combination is effectively applied to the analysis of network traffic data to detect slow and distributed scans, which is a difficult-to-detect form of cyberattack. Our approach is sufficiently general to be applied to a diverse set of data understanding problems as well as used in conjunction with a diverse set of analysis and visualization tools
