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)


Workshop Program (Monday, Nov 11, 2019)


  • 08:00AM - 09:00AM: Registration and Breakfast

  • 09:00AM - 09:05AM: Opening Remarks

  • 09:05AM - 10:05AM: Keynote Session
    • Speaker: Dr. Cliff Wang, Computing Sciences Division Chief, Army Research Office, USA
      Title: Cyber Deception: An Emergent Research Area

      Abstract: Deception has been used since early days of warfare. Until most recently deception techniques such as honeynet have also been adopted in the cyber space. However, most approaches are ad hoc at best. There are increasing interests in establishing a formal model that can be used to capture key characteristics of cyber deception and to evaluate the effectiveness of using deception as a proactive network defense tool. This talk will share some of initial thoughts in identifying new opportunities in cyber deception and exploring new research ideas in this space.

      Bio: As the division chief of ARO Computing Sciences division, Dr. Wang heads the division staff and manages resources to execute the Army's basic research investment in Computing Sciences with an annual budget of over 20 million dollars. To explore the frontiers of computing, he leads the extramural program to help establish scientific foundation of information sciences and to create new knowledge in the field. Dr. Cliff Wang graduated from North Carolina State University with a PhD in computer engineering in 1996. He has been carrying out research in the area of computer vision, medical imaging, high speed networks, and most recently information security. He has authored over 50 technical papers and 3 Internet standards RFCs. Dr. Wang also authored/edited for 18 books in the area of information security and hold 4 US patents on information security system development. Since 2003, Dr. Wang has been managing extramural research portfolio on information assurance at US Army Research Office. In 2007, he was selected as the chief of the computing sciences division at ARO while in the same time managing his program in cyber security. For the past 15 years, Dr. Wang managed over $200M research funding which led to significant technology breakthroughs. Dr. Wang also holds adjunct professor appointment at both Department of Computer Science and Department of Electrical and Computer Engineering at North Carolina State University. Dr. Wang is a Fellow of IEEE.

  • 10:05AM - 10:30AM: Break

  • 10:30PM - 12:00PM: Session 1 (Modeling, Analysis and Evaluation)
    • "On the Resilience of Network-based Moving Target Defense Techniques Against Host Profiling Attacks"
    • Michal Piskozub, Riccardo Spolaor (University of Oxford, UK), Mauro Conti (University of Padua, Italy) and Ivan Martinovic (University of Oxford, UK)
    • "Specification-driven Moving Target Defense Synthesis"
    • Md Mazharul Islam, Qi Duan and Ehab Al-Shaer (University of North Carolina at Charlotte, USA)
    • "Bayesian Stackelberg Game for Risk-aware Edge Computation Offloading"
    • Yang Bai, Lixing Chen (University of Miami), Linqi Song (City University of Hong Kong) and Jie Xu (University of Miami)

  • 12:00PM - 02:00PM: Lunch Break

  • 02:00PM - 03:30PM: Session 2 (Frameworks and Methods)
    • "A Scalable High Fidelity Decoy Framework against Sophisticated Cyber Attacks"
    • Jianhua Sun (College of William and Mary, USA), Songsong Liu and Kun Sun (George Mason University, USA).
    • "Run or Hide? Both! A Method Based on IPv6 Address Switching to Escape While Being Hidden"
    • Maxime Ayrault, Etienne Borde and Ulrich Kuhne (Telecom Paris, Institut Polytechnique de Paris).
    • "A cost-effective shuffling method against DDoS attacks using Moving Target Defense"
    • Yuyang Zhou, Guang Cheng, Shanqing Jiang, Ying Hu, Yuyu Zhao and Zihan Chen (Southeast University, China)

  • 03:30PM - 04:00PM: Break

  • 04:00PM - 05:00PM: Session 3 (Strategies and Applications)
    • "A Collaborative Strategy for Mitigating Tracking Through Browser Fingerprinting"
    • Alejandro Gomez-Boix, Davide Frey, David Bromberg (Univ Rennes, Inria, CNRS, IRISA, France), and Benoit Baudry (KTH Royal Institute of Technology, Sweden)
    • "Should I (re)Learn or Should I Go(on)? Stream Machine Learning for Adaptive Defense against Network Attacks"
    • Pedro Casas (Austrian Institute of Technology), Pavol Mulinka (CTU Prague, Czechia) and Juan Vanerio (Universidad de la Republica, Uruguay)


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