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MTD 2015 : 2nd ACM Workshop on Moving Target Defense


When Oct 12, 2015 - Oct 12, 2015
Where Denver, Colorado, USA
Submission Deadline Jun 8, 2015
Notification Due Jul 19, 2015
Final Version Due Jul 29, 2015
Categories    security

Call For Papers

The static nature of current computing systems has made them easy to attack and harder to defend. Adversaries have an asymmetric

advantage in that they have the time to study a system, identify its vulnerabilities, and choose the time and place of attack to gain

the maximum benefit. The idea of moving-target defense (MTD) is to impose the same asymmetric disadvantage on attackers by making

systems dynamic and therefore harder to explore and predict. With a constantly changing system and its ever adapting attack surface,

attackers will have to deal with a great deal of uncertainty just like defenders do today. The ultimate goal of MTD is to increase the

attackers' workload so as to level the cybersecurity playing field for both defenders and attackers - hopefully even tilting it in

favor of the defender.

Workshop Goals
This workshop seeks to bring together researchers from academia, government, and industry to report on the latest research efforts on

moving-target defense, and to have productive discussion and constructive debate on this topic. We solicit submissions on original

research in the broad area of MTD, with possible topics such as those listed below. Since MTD research is still in its nascent stage,

the list should only be used as a reference. We welcome all works that fall under the broad scope of moving target defense, including

research that shows negative results. Below are examples of appropriate topics for MTD 2015:

+ MTD Techniques
- System randomization
- Artificial diversity and system diversification
- Bio-inspired MTDs
- Dynamic network configuration
- Cloud-based and Large-scale MTDs (using multiple techniques)
- Autonomous technologies for MTD
- Dynamic compilation
- Moving targets in software coding, application APIs and virtualization
+ MTD Modeling and Analysis
- Analytical models for MTDs
- Quantitative models and effective measurement of MTDs
- Theoretic study on modeling trade-offs of using MTD approaches
- Control and game theory aspects of deploying MTDs
- Human, social, and psychology aspects of MTD
- Other related MTD areas

Paper submissions
Submitted papers must not substantially overlap papers that have been published or that are simultaneously submitted to a journal or a

conference with proceedings. Submissions should be at most 10 pages in the ACM double-column format, excluding well-marked appendices,

and at most 12 pages in total. Submissions are not required to be anonymized.

Submissions are to be made by June 8, 2015 through Only PDF files will be

accepted. Submissions not meeting these guidelines risk rejection without consideration of their merits. Authors of accepted papers

must guarantee that one of the authors will register and present the paper at the workshop. Proceedings of the workshop will be

available on a CD to the workshop attendees and will become part of the ACM Digital Library.

TPC Co-Chairs
George Cybenko, Dartmouth College, USA
Dijiang Huang, Arizona State University, USA

Publicity Chair
Massimiliano Albanese, George Mason University

Important dates
Paper submission due: June 8, 2015
Notification to authors: July 19, 2015
Camera ready due: July 29, 2015
Workshop dates: October 12, 2015

Program Committee
Gail-Joon Ahn, Arizona State University
Ehab Al-Shaer, University of North Carolina Charlotte
Massimiliano Albanese, George Mason University
Hasan Cam, Army Research Lab
Marco Carvalho, Florida Institute of Technology
Scott DeLoach, Kansas State University
Yuval Elovici, Ben-Gurion University of the Negev
Rob Erbacher, Army Research Lab
Michael Franz, University of California, Irvine
Robert Gray, BAE Systems
Jason R. Hamlet, Sandia Lab
Sushil Jajodia, George Mason University
Myong Kang, Naval Research Lab
Angelos Keromytis, Columbia University
Christopher Charles Lamb, Sandia Lab
Jason Li, Intelligent Automation, Inc. (IAI)
Peng Liu, Penn State University
Tom Longstaff, John Hopkins University
Lisa Marvel, Amy Research Lab
Patrick McDaniel, Penn State University
Prasant Mohapatra, UC Davis
Sanjai Narain, Applied Communication Sciences
Hamed Okhravi, MIT Lincoln Laboratory
Xinming Ou, Kansas State University
Radha Poovendran, University of Washington
Kui Ren, University at Buffalo
Kun Sun, College of William and Mary
Vipin Swarup, MITRE
Jason Syversen, Siege Technologies
Cliff Wang, Army Research Office

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