Center for Cyber Security Research|
Projects |
Recent
News:
·
(Upcoming)
Prof. Anil K. Jain from
· (Dec. 2007) Congratulations to Prof. Bill Harrison for his NSF CAREER award for his project entitled "Automated Synthesis of High-Assurance Security Kernels".
The establishment of the Center for Cyber Security Research (CCSR) leverages the nationally recognized expertise of MU researchers in Computer Science, Information Technology, Engineering, Learning and Education, Journalism, and Health Informatics. Members of this diverse team have been addressing various basic research issues in cyber security, including information assurance, secure and forensic multimedia, home network and sensor network security, reliability and survivability of large scale distributed information systems, secure cross-institution resource sharing and collaboration, and application and software security. We will also address the application of cyber security technologies to secure tele-health, e-publishing, and e-learning. Some of these efforts have been supported by NSF and NIH. CCSR is intended to streamline our research and education effort in this critical area, to foster academic, industrial, and government laboratory participation in multidisciplinary cyber security research. Our long term goal is to develop a nationally recognized research and training program with substantial cyber security research capacity addressing the security requirements of homeland security, health informatics, and general e-commerce. CCSR will provide an excellent environment for BSIT students to receive cyber security training and to perform cyber security research. To address the imminent needs of the security workforce, the center will build training and certifications programs for cyber security.
Gilliom Cyber Security Gift Fund:
The center has recently received a gift fund from Mr. Greg L. Gilliom to enhance cyber security research and education in the Computer Science Department at the University of Missouri-Columbia. This gift fund supports the operation of the center, a seminar series, graduate fellowships, and undergraduate scholarships.
Affiliated
members:
· Prof. Wenjun Zeng, Dept. of Computer Science (Director)
· Prof. Xinhua Zhuang, Dept. of Computer Science
· Prof. Michael Jurczyk, Dept. of Computer Science
· Prof. Gordon Springer, Dept. of Computer Science
· Prof. Bill Harrison, Dept. of Computer Science
· Prof. Haibin Lu, Dept. of Computer Science
· Prof. Kannappan. Palaniappan, Dept. of Computer Science
· Prof. Yunxin Zhao, Dept. of Computer Science
· Prof. Yi Shang, Dept. of Computer Science
· Prof. Zhihai He, Dept. of Electrical & Computer Engineering
· Prof. Sheila A. Grant , Dept. of Biological Engineering
· Prof. Joi Moore, SISLT
· Prof. Kwangsu Cho, SISLT
·
Prof. Tom
Warhover,
· Prof. Charles Caldwell, Health informatics
· Prof. Karen E. Edison, Missouri Telehealth Network
· Allen Brokken, Division of IT
Gilliom Cyber
Security Fellows (2007-2008):
· Ilker Ersoy
· Wei Liu
· Mian Pan
· Adam Procter
· Peng Zhuang
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Speaker: Prof. Anil
K. Jain,
Room: W1004, Lafferre Hall Abstract: Can Biography: Anil Jain is a University
Distinguished Professor in the Department of Computer Science at |
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Speaker: Wei Liu, UMC Tuesday, Oct. 2, 2007, 4-5 pm, 222 EBW Abstract: Digital watermarking is an efficient and promising approach to protect intellectual property rights of digital media. Spread spectrum (SS) is one of the most widely used image watermarking schemes because of its robustness against attacks and its support for the exploitation of the properties of the human visual system (HVS). To maximize the watermark strength without introducing visual artifacts, in SS watermarking, the watermark signal is usually modulated by the so-called just-noticeable difference (JND) of the host image. In advanced perceptual models, the JND is characterized as a non-linear function of local image features. The optimum detection scheme for such non-linearly embedded watermarks, however, has rarely been studied. In this talk, we address this problem by deriving an optimum generalized correlation detector (GCD). The performance of a GCD is analyzed and the optimum GCD is solved according to the Neyman-Pearson criterion. We also show that the locally optimum detector (LOD) is always in the form of GCD, thus the optimality of our solution is confirmed. By using the proposed detector, we are able to deal with arbitrary host signal distributions and arbitrary JND models that exploit the self-masking property of the HVS. Simulation results demonstrate the superior performances of the proposed detector over the conventional linear correlation detector (LCD).
Biography: Wei Liu received his B.E. and
M.E. degrees from |
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