ACM MTD 2016

Third ACM Workshop on Moving Target Defense (MTD 2016)

In conjunction with the 23rd ACM Conference on Computer and Communications =
Security (CCS)

October 24-28, 2016, Vienna, Austria

           PAPER SUBMISSION DEADLINE July 27, 2016

The static nature of current computing systems has made them easy to attack=
 and harder to defend. Adversaries have an asymmetric advantage in that the=
y 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 movi=
ng-target defense (MTD) is to impose the same asymmetric disadvantage on at=
tackers by making systems dynamic and therefore harder to explore and predi=
ct. With a constantly changing system and its ever adapting attack surface,=
 attackers will have to deal with a great deal of uncertainty just like def=
enders do today. The ultimate goal of MTD is to increase the attackers' wor=
kload so as to level the cybersecurity playing field for both defenders and=
 attackers - hopefully even tilting it in favor of the defender.

This workshop seeks to bring together researchers from academia, government=
, and industry to report on the latest research efforts on moving-target de=
fense, and to have productive discussion and constructive debate on this to=
pic. We solicit paper and system demo submissions on original research in t=
he broad area of MTD, with possible topics such as those listed below. Sinc=
e 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 mo=
ving target defense, including research that shows negative results.

System randomization
Artificial diversity
Cyber maneuver
Bio-inspired defenses
Dynamic network configuration
Moving target in the cloud
System diversification techniques
Dynamic compilation techniques
Adaptive defenses
MTD quantification methods and models
Large-scale MTD (using multiple techniques)
Moving target in software coding, application APIs virtualization
Autonomous technologies for MTD
Theoretic study on modeling trade-offs of using MTD
Human, social, and psychology aspects of MTD
Other related areas

Submission web site: Both paper and system demo submissions are to be made =
to the submission web site at
Submissions should be at most 10 pages in the ACM double-column format, exc=
luding well-marked appendices, and at most 12 pages in total. Submissions a=
re not required to be anonymized.

Paper submission due: July 27, 2016 [FIRM]
Notification to authors: September 5, 2016
Camera ready due: September 12, 2016
Workshop date: October 24, 2016


Program Co-Chairs
Peng Liu, Penn State University
Cliff Wang, U.S. Army Research Office

Gail-Joon Ahn, Arizona State University
Massimiliano Albanese, George Mason University
Hasan Cam, U.S. Army Research Laboratory
Ping Chen, Pennsylvania State University
Scott A. Deloach, Kansas State University
Robert Erbacher, Army Research Laboratory
Michael Franz, University of California, Irvine
Jason Hamlet, Sandia National Laboratories
Trent Jaeger,  Pennsylvania State University
Sushil Jajodia, George Mason University
Myong Kang, NRL
Srikanth Krishnamurthy, University of California, Riverside
Christopher Lamb, University of New Mexico
Karl Levitt, University of California, Davis
Jason Li, Intelligent Automation Inc.
Zhuo Lu, University of Memphis
Patrick McDaniel, Penn State University
Sanjai Narain, Applied Communication Sciences
Iulian Neamtiu, University of California, Riverside
Hamed Okhravi, MIT Lincoln Laboratory
Simon Ou, University of South Florida
Vipin Swarup, MITRE, USA
Kun Sun,  College of William and Mary
Jason Syversen, Siege Technologies
Michael Wellman, University of Michigan
Minghui Zhu, Pennsylvania State University