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
Submissions
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 https://mtd2021.hotcrp.com. 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 25, 2021 to be considered. Notification of acceptance or rejection will be sent to authors by August 13, 2021. Camera ready papers must be submitted by September 6, 2021. 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 25, 2021July 13, 2021 - Notification to authors: August 13, 2021
- Camera ready due: September 6, 2021 (No Extensions)
Keynote Speakers
Prasant Mohapatra, UC Davis, Moving Target Defense against Adversarial Machine Learning
Program Chairs
- Trent Jaeger, Penn State University, USA
- Zhiyun Qian, University of California, Riverside, USA
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
- Alina Oprea, Northeastern University, USA
- Chengyu Song, UC Riverside, USA
- Cliff Wang, Army Research Office & North Carolina State University, USA
- Gabi Dreo, Uni Bundeswehr Munich, Germany
- Hamed Okhravi, MIT Lincoln Laboratory, USA
- Jie Fu, Worcester Polytechnic Institute, USA
- Kun Sun, George Mason University, USA
- Nathan Burow, MIT Lincoln Laboratory, USA
- Peng Liu, Penn State University, USA
- Per Larsen, Immunant, Inc. & UC Irvine, USA
- Sailik Sengupta, Amazon AI, USA
- Sandra Rueda, Universidad de los Andes, Colombia
- Tom La Porta, Penn State University
- Valentina Casola, University of Napoli Federico II, Italy
- Vipin Swarup, MITRE, USA
- Ziming Zhao, University at Buffalo
Program
The complete proceedings of the workshop will be made available online.
- MTD 2021, 5pm – 10:20pm EST, November 14, 2021
- Welcome Remarks: 5pm – 5:15pm EST
- Session 1: 5:15pm – 6:30 EST
- 5:15pm – 5:40pm, Randomization-based Defenses against Data-Oriented Attacks, Stijn Volckaert (KU Leuven)
- 5:40pm – 6:05pm, “What’s in the box?!”: Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Models, Sahar Abdelnabi and Mario Fritz (CISPA)
- 6:05pm – 6:30pm, Combinatorial Boosting of Classifiers for Moving Target Defense Against Adversarial Evasion Attacks, Rauf Izmailov, Peter Lin, Sridhar Venkatesan, Shridatt Sugrim (Peraton Labs)
- Break: 6:30pm – 6:45pm EST
- Session 2: 6:45pm – 8:00pm EST
- 6:45pm – 7:10pm, Game-Theoretic Models for Cyber Deception, Fei Fang (CMU)
- 7:10pm – 7:35pm, Using Honeypots to Catch Adversarial Attacks on Neural Networks, Shawn Shan (University of Chicago)
- 7:35pm – 8:00pm, Research frontiers for Moving Target Defenses, Nathan Burow (MIT Lincoln Laboratory)
- Break: 8:00pm – 8:15pm EST
- Keynote: 8:15pm – 9:15pm EST
- Moving Target Defense against Adversarial Machine Learning, Prasant Mohapatra (UC Davis)
- Break: 9:15pm – 9:30pm EST
- Session 3: 9:30pm-10:20pm EST
- 9:30pm – 9:55pm, Themis: Ambiguity-Aware Network Intrusion Detection based on Symbolic Model Comparison, Zhongjie Wang (Baidu USA)
- 9:55pm – 10:20pm Concolic Execution of NMap Scripts for Honeyfarm Generation, Zhe Li, Bo Chen, Wu-chang Feng, Fei Xie (Portland State University)