Call for Papers

The static nature of current computing systems has made them easy to attack and hard 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 random, diverse, and 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 significant 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 defenders and attackers – ultimately tilting it in favor of the defender.

The 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.  As MTD research is still in its infancy, the list should only be used as a reference.  We welcome all contributions that fall under the broad scope of moving target defense, including research that shows negative results.

  • System randomization
  • Artificial diversity
  • Cyber maneuver and agility
  • Software diversity
  • Dynamic network configuration
  • Moving target in the cloud
  • System diversification techniques
  • Dynamic compilation techniques
  • Adaptive/proactive defenses
  • Intelligent countermeasure selection
  • MTD strategies and planning
  • Deep learning for MTD
  • MTD quantification methods and models
  • MTD evaluation and assessment frameworks
  • Large-scale MTD (using multiple techniques)
  • Moving target in software coding, application API virtualization
  • Autonomous technologies for MTD
  • Theoretic study on modeling trade-offs of using MTD approaches
  • Human, social, and usability aspects of MTD
  • AI, machine learning, and data analytics related MTD
  • Other related areas


Submitted papers must not substantially overlap with papers that have been published or 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 to the submission web site at  Only PDF files will be accepted.  Submissions not meeting these guidelines risk rejection without consideration of their merits.  Papers must be received by the deadline of June 21, 2020 to be considered.  Notification of acceptance or rejection will be sent to authors by August 8, 2020.  Camera ready papers must be submitted by September 2, 2020.  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.

Important Dates

  • Paper submission due: June 21, 2020 July 19, 2020
  • Notification to authors: August 8, 2020 August 10, 2020
  • Camera ready due: September 6, 2020 (No Extensions)

Keynote Speakers

To be announced

Program Chairs

Steering Commitee

  • Sushil Jajodia, Chair, George Mason University, USA
  • Dijiang Huang, Arizona State University, USA
  • Hamed Okhravi, MIT Lincoln Laboratory, USA
  • Xinming Ou, University of South Florida, USA
  • Kun Sun, George Mason University, USA

Program Commitee

  • Massimiliano Albanese, George Mason University, USA
  • Alex  Bardas, University of Kansas, USA
  • Hasan Cam, US Army Research Lab, USA
  • Valentina  Casola, University of Naples Federico II, Italy
  • Joel Coffman, US Air Force Academy, USA
  • George Cybenko, Dartmouth College, USA
  • Michael Franz, UC Irvine, USA
  • Dijiang Huang, Arizona State University, USA
  • Dong Seong Kim, University of Canterbury, New Zealand
  • Jason Li, Seige Technology, USA
  • Peng Liu, Penn State University, USA
  • Zhuo Lu, University of South Florida, USA
  • Sandeep Pisharody, MIT Lincoln Laboratory, USA
  • Kun Sun, George Mason University, USA
  • Vipin Swarup, MITRE Corporation, USA
  • Shouhuai Xu, University of Texas at San Antonio, USA
  • Minghui Zhu, Penn State University, USA


To be announced