UT Dallas researcher gets introspective about virtual machines

UT Dallas researcher gets introspective about virtual machines
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A University of Texas at Dallas researcher has come up with a way for virtual machines to have each others' backs in the name of better cloud network security.

Dr. Zhiqiang Lin, an assistant professor of computer science at the Erik Jonsson School of Engineering and Computer Science at UT Dallas, has earned a National Science Foundation Faculty Early Career Development (CAREER) Award to support his efforts in the area of virtual machine introspection. The award includes $500,000 in funding for five years.

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"Dr. Lin's technical achievements are significant," said Dr. Bhavani Thuraisingham, executive director of the UT Dallas Cyber Security Research and Education Institute and the Louis A. Beecherl Jr. Distinguished Professor in the Jonsson School, in a statement. "He has developed a highly novel method to carry out virtual machine introspection and will be exploring this area further."

Dr. Zhiqiang Lin virtual machine introspection University of Texas at Dallas

Dr. Zhiqiang Lin earned a $500K award to further his research into virtual machine security

According to UT Dallas:  "One challenge in virtual machine introspection is that a semantic gap exists between the data structures that the virtual machine understands and the raw bytes viewable from the outside that provide little contextual information. With the CAREER Award, Lin will develop principles and techniques to automatically bridge the gap."

Lin, who has had past research funded in part by virtualization stalwart VMware, says he is shooting for a less manual approach to virtual security -- an approach that involves reusing compiled code to help bring visibility into virtual machines.

The issue of security in virtualized environments has been raised anew with the introduction of container technology, such as Docker. And in fact, there has been a call for providers of container deployment tools to make more inroads on the introspection front.

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