Join us in person!

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. In addition, this year, we also solicit submissions for Systematization of Knowledge. These submissions will be reviewed similar to regular submissions. We also welcome all contributions that fall under the broad scope of moving target defense, including research that shows negative results.

List of Broad Topics:

  • 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
  • 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 to MTD
  • MTD used to enhance machine learning security, privacy, and robutness
  • Other related areas

Systematization of Knowledge: In addition to the regular submissions, we also seek systematization of knowledge submissions for MTD’22. Such submissions can evaluate and contextualize a subdomain of MTD or highlight important insights and lessons learned. These submissions will be reviewed based on their insights and treatment of the subdomain and not based on original research. All systematization of knowledge submissions must be distinguished by the prefix “SoK”. 


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. SoK submissions could be at most 15 pages long, excluding well-marked appendices, and at most 17 pages in total.

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 July 12, 2022 to be considered.  Notification of acceptance or rejection will be sent to authors by August 10, 2022.  Camera ready papers must be submitted by September 6, 2022.  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: July 12, 2022 July 26, 2022
  • Notification to authors: August 10, 2022 August 17, 2022
  • Camera ready due: September 6, 2022  September 18, 2022

Keynote Speakers

Prof. Cristina Nita-Rotaru, Professor of Computer Science at Khoury College of Computer Science at Northeastern University, USA

Title: "Dynamic Security with SDN: Opportunities, Challenges, and Lessons Learned"

Dr. Nicolas Papernot, Assistant Professor, Department of Electrical and Computer Engineering, University of Toronto, Canada

Title: "The Role of Randomization in Trustworthy Machine Learning"

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
  • Alex Bardas, University of Kansas
  • Nathan Burow, MIT Lincoln Laboratory
  • Valentina Casola, University of Naples Federico II
  • Dong Seong Kim, The University of Queensland
  • Thomas La Porta, The Pennsylvania State University
  • Peng Liu, The Pennsylvania State University
  • Alina Oprea, Northeastern University
  • Xinming Ou, University of South Florida
  • Chengyu Song, UC Riverside
  • Kun Sun, George Mason University
  • Vipin Swarup, The MITRE Corporation
  • Ziming Zhao, University at Buffalo


All times are in US Pasific Time (UTC-8:00)

Complete Proceedings of MTD'22

Welcome Remarks Hamed Okhravi and Cliff Wang

 9:15am - 9:30am

Keynote 1

Dynamic Security with SDN: Opportunities, Challenges, and Lessons Learned

Cristina Nita-Rotaru


 9:30am - 10:30am



10:30am - 10:45am

Session 1: New  Techniques 

Rave: A Modular and Extensible Framework for Program State Re-Randomization

Christopher Blackburn, Xiaoguang Wang and Binoy Ravindran

10:45am - 11:15am


Multi-variant Execution at the Edge

Javier Cabrera Arteaga, Pierre Laperdrix, Martin Monperrus and Benoit Baudry

11:15am - 11:45am

Lunch and Networking


11:45am - 1:00pm

Keynote 2

The Role of Randomization in Trustworthy Machine Learning

Nicholas Papernot

1:00pm - 2:00pm

Session 2:  Insights and Lessons Learned

Hardware Moving Target Defenses against Physical Attacks: Design Challenges and Opportunities

David Koblah, Fatemeh Ganji, Demenic Forte, and Shahin Tajik

2:00pm - 2:30pm


On Randomization in MTD Systems

Majid Ghaderi, Samuel Jero, Cristina Nita-Rotaru, and Reihaneh Safavi-Naini

2:30pm - 3:00pm



3:00pm - 3:15pm

Session 3: Analysis and Evaluation


Evaluating Deception and Moving Target Defense with Network Attack Simulation

Daniel Reti, Daniel Fraunholz, Karina Elzer, Hans Dieter Schotten and Daniel Schneider

3:15pm - 3:45pm


Reasoning about Moving Target Defense in Attack Modeling Formalisms

Gabriel Ballot, Vadim Malvone, Jean Leneutre and Etienne Borde

3:45pm - 4:15pm


Game Theory Approaches to Evaluating the Deception-based Moving Target Defense

Duohe Ma, Zhimin Tang, Xiaoyan Sun, Lu Guo, Liming Wang and Kai Chen

4:15pm - 4:45pm

Closing Remarks

Hamed Okhravi and Cliff Wang

4:45pm - 5:00pm