The 6th ACM Workshop on Moving Target Defense (MTD 2019)
London, UK, November 11, 2019

In conjunction with the 26th ACM Conference on Computer and Communications Security (ACM CCS 2019)

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 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 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
  • Other related areas

Important Dates

Paper submission due: June 28, 2019 July 12, 2019 (AoE)

Notification to authors: August 2, 2019 August 9, 2019

Camera ready due: August 30, 2019

Paper Submission

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 (see https://www.acm.org/publications/proceedings-template), 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 https://easychair.org/conferences/?conf=mtd2019. 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 in the ACM Digital Library.


Program Committee Chair

  • Zhuo Lu, University of South Florida, USA

Steering Committee

  • 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

Technical Program Committee

  • Massimiliano Albanese, George Mason University, USA
  • Alex Bardas, University of Kansas, USA
  • Valentina Casola, University of Naples, Italy
  • Joel Coffman, United States Air Force Academy, USA
  • Michael Franz, University of California, Irvine, USA
  • DongSeong (Dan) Kim, University of Queensland, Australia
  • Christopher Lamb, University of New Mexico, USA
  • Jason Li, Intelligent Automation Inc, USA
  • Zhuo Lu, University of South Florida, USA
  • Peng Liu, Penn State University, USA
  • Hamed Okhravi, MIT Lincoln Laboratory, USA
  • Sandeep Pisharody, MIT Lincoln Laboratory, USA
  • Kun Sun, George Mason University, USA
  • Vipin Swarup, MITRE, USA
  • Cliff Wang, Army Research Office, USA
  • Jie Wang, North Carolina State University, USA
  • Jie Xu, University of Miami, USA
  • Minghui Zhu, Penn State University, USA

Program