skip to main content
10.1145/3387902.3397225acmconferencesArticle/Chapter ViewAbstractPublication PagescfConference Proceedingsconference-collections
research-article

A critical view on moving target defense and its analogies

Published: 23 May 2020 Publication History

Abstract

In the last decade the Moving Target Defense (MTD) has gained popularity as a new cyber security defense paradigm. Moving Target Defense (MTD) intends to change the (presumable) information asymmetry between attacker and defender in favor of the defender by constantly changing a network's appearance as to invalidate previously acquired information. Many papers discussing MTD have been proposed and in recent years MTD techniques for a wide range of applications in enterprise networks, Cloud environments, IoT, automotive CAN buses and smart grids have been proposed. In these papers, MTD is often introduced as a "game changer" and explained with help of nice figurative analogies. Yet, how useful are these repeated changes really to deter the attacker? And even more importantly, could it not be that there are downsides to the constant changes that, ultimately, degrade security? In this position paper we argue that one needs to have a more critical and open minded view on MTD. There are MTD techniques that improve security. But we also provide several examples where they reduce security or where movement does not matter at all and is only introduced as to label a given technique as MTD.

References

[1]
[n.d.]. US DHS Homepage. https://www.dhs.gov/science-and-technology/csd-mtd. [Online; accessed 6-March-2020].
[2]
O. Abdel Wahab, J. Bentahar, H. Otrok, and A. Mourad. 2019. Resource-Aware Detection and Defense System Against Multi-Type Attacks in the Cloud: Repeated Bayesian Stackelberg Game. IEEE Transactions on Dependable and Secure Computing (2019), 1--1.
[3]
Noor O. Ahmed and Bharat Bhargava. 2016. Mayflies: A Moving Target Defense Framework for Distributed Systems. In Proceedings of the 2016 ACM Workshop on Moving Target Defense (Vienna, Austria) (MTD '16). ACM, 59--64.
[4]
N. O. Ahmed and B. Bhargava. 2018. From Byzantine Fault-Tolerance to Fault-Avoidance: An Architectural Transformation to Attack and Failure Resiliency. IEEE Transactions on Cloud Computing (2018), 1--1.
[5]
N. O. Ahmed and B. Bhargava. 2020. Bio-inspired Formal Model for Space/Time Virtual Machine Randomization and Diversification. IEEE Transactions on Cloud Computing (2020), 1--1.
[6]
Airwolfhound. [n.d.]. Starling Murmuration - Eastbridge. https://www.flickr.com/photos/24874528@N04/45519155125/. [Online; accessed 23-April-2020].
[7]
Hooman Alavizadeh, Dong Seong Kim, Jin B. Hong, and Julian Jang-Jaccard. 2017. Effective Security Analysis for Combinations of MTD Techniques on Cloud Computing (Short Paper). In Information Security Practice and Experience. Springer, 539--548.
[8]
Hooman Alavizadeh, Dong Seong Kim, and Julian Jang-Jaccard. 2019. Modelbased evaluation of combinations of Shuffle and Diversity MTD techniques on the cloud. Future Generation Computer Systems (2019).
[9]
Ramazan Algin, Huseyin O. Tan, and Kemal Akkaya. 2017. Mitigating Selective Jamming Attacks in Smart Meter Data Collection Using Moving Target Defense. In Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks (Miami, Florida, USA) (Q2SWinet '17). ACM, 1--8.
[10]
H. M. J. Almohri, L. T. Watson, and D. Evans. 2018. Misery Digraphs: Delaying Intrusion Attacks in Obscure Clouds. IEEE Transactions on Information Forensics and Security 13, 6 (June 2018), 1361--1375.
[11]
N. Anderson, R. Mitchell, and I. R. Chen. 2016. Parameterizing Moving Target Defenses. In 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS). 1--6.
[12]
Alexander Bajic and Georg T. Becker. 2018. Attack Simulation for a Realistic Evaluation and Comparison of Network Security Techniques. In Secure IT Systems. Springer, 236--254.
[13]
Stephen W. Boyd and Angelos D. Keromytis. 2004. SQLrand: Preventing SQL Injection Attacks. In Applied Cryptography and Network Security. Springer Berlin Heidelberg, Berlin, Heidelberg, 292--302.
[14]
Valentina Casola, Alessandra De Benedictis, and Massimiliano Albanese. 2013. A moving target defense approach for protecting resource-constrained distributed devices. In IEEE 14th International Conference on Information Reuse & Integration (IRI). IEEE.
[15]
S. Chang, Y. Park, and B. B. Ashok Babu. 2019. Fast IP Hopping Randomization to Secure Hop-by-Hop Access in SDN. IEEE Transactions on Network and Service Management 16, 1 (March 2019), 308--320.
[16]
Ankur Chowdhary, Adel Alshamrani, Dijiang Huang, and Hongbin Liang. 2018. MTD Analysis and Evaluation Framework in Software Defined Network (MASON). In Proceedings of the 2018 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization (Tempe, AZ, USA) (SDN-NFV Sec '18). ACM, 43--48.
[17]
Warren Connell, Massimiliano Albanese, and Sridhar Venkatesan. 2017. A Framework for Moving Target Defense Quantification. In IFIP International Conference on ICT Systems Security and Privacy Protection. Springer, 124--138.
[18]
Warren Connell, Daniel A. Menascé, and Massimiliano Albanese. 2017. Performance Modeling of Moving Target Defenses. In Proceedings of the 2017 Workshop on Moving Target Defense (Dallas, Texas, USA) (MTD 17). ACM, 53--63.
[19]
M. Dunlop, S. Groat, W. Urbanski, R. Marchany, and J. Tront. 2011. MT6D: A Moving Target IPv6 Defense. In 2011 - MILCOM 2011 Military Communications Conference. 1321--1326.
[20]
J. B. Hong and D. S. Kim. 2016. Assessing the Effectiveness of Moving Target Defenses Using Security Models. IEEE Transactions on Dependable and Secure Computing 13, 2 (March 2016), 163--177.
[21]
White House. 2011. Trustworthy Cyberspace: Strategic Plan for the Federal Cybersecurity Research and Development Program. Report of the National Science and Technology Council, Executive Office of the President (2011).
[22]
Todd Jackson, Andrei Homescu, Stephen Crane, Per Larsen, Stefan Brunthaler, and Michael Franz. 2013. Diversifying the Software Stack Using Randomized NOP Insertion. In Moving Target Defense II. Springer New York, New York, NY, 151--173.
[23]
Todd Jackson, Babak Salamat, Andrei Homescu, Karthikeyan Manivannan, Gregor Wagner, Andreas Gal, Stefan Brunthaler, Christian Wimmer, and Michael Franz. 2011. Compiler-Generated Software Diversity. Springer New York, New York, NY, 77--98.
[24]
X. Jiang, H. J. Wangz, D. Xu, and Y. M. Wang. 2007. RandSys: Thwarting Code Injection Attacks with System Service Interface Randomization. In 26th IEEE International Symposium on Reliable Distributed Systems (SRDS 2007). 209--218.
[25]
David J. John, Robert W. Smith, William H. Turkett, Daniel A. Cañas, and Errin W. Fulp. 2014. Evolutionary Based Moving Target Cyber Defense. In Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation (Vancouver, BC, Canada) (GECCO Comp '14). ACM, 1261--1268.
[26]
Gaurav S. Kc, Angelos D. Keromytis, and Vassilis Prevelakis. 2003. Countering Code-injection Attacks with Instruction-set Randomization. In Proceedings of the 10th ACM Conference on Computer and Communications Security (Washington D.C., USA) (CCS '03). ACM, 272--280.
[27]
D. Kewley, R. Fink, J. Lowry, and M. Dean. 2001. Dynamic approaches to thwart adversary intelligence gathering. In DARPA Information Survivability Conference amp; Exposition II, 2001. DISCEX '01, Vol. 1. 176--185 vol.1.
[28]
Cheng Lei, Hong-Qi Zhang, Li-Ming Wan, Lu Liu, and Duo he Ma. 2018. Incomplete information Markov game theoretic approach to strategy generation for moving target defense. Computer Communications 116 (2018), 184 -- 199.
[29]
Jason Li, Justin Yackoski, and Nicholas Evancich. 2016. Moving Target Defense: A Journey from Idea to Product. In Proceedings of the 2016 ACM Workshop on Moving Target Defense (Vienna, Austria) (MTD '16). ACM, 69--79.
[30]
Brian Lucas, Errin W. Fulp, David J. John, and Daniel Cañas. 2014. An Initial Framework for Evolving Computer Configurations As a Moving Target Defense. In Proceedings of the 9th Annual Cyber and Information Security Research Conference (Oak Ridge, Tennessee, USA) (CISR '14). ACM, 69--72.
[31]
Douglas C. MacFarland and Craig A. Shue. 2015. The SDN Shuffle: Creating a Moving-Target Defense Using Host-based Software-Defined Networking. In Proceedings of the Second ACM Workshop on Moving Target Defense (Denver, Colorado, USA) (MTD '15). ACM, 37--41.
[32]
Hoda Maleki, Saeed Valizadeh, William Koch, Azer Bestavros, and Marten van Dijk. 2016. Markov Modeling of Moving Target Defense Games. In Proceedings of the 2016 ACM Workshop on Moving Target Defense (Vienna, Austria) (MTD '16). ACM, 81--92.
[33]
J. Narantuya, S. Yoon, H. Lim, J. Cho, D. S. Kim, T. Moore, and F. Nelson. 2019. SDN-Based IP Shuffling Moving Target Defense with Multiple SDN Controllers. In 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S). 15--16.
[34]
F. Nizzi, T. Pecorella, F. Esposito, L. Pierucci, and R. Fantacci. 2019. IoT Security via Address Shuffling: The Easy Way. IEEE Internet of Things Journal 6, 2 (April 2019), 3764--3774.
[35]
H. Okhravi, T. Hobson, D. Bigelow, and W. Streilein. 2014. Finding Focus in the Blur of Moving-Target Techniques. IEEE Security Privacy 12, 2 (Mar 2014), 16--26.
[36]
Oxfordian. [n.d.]. Landscape with deerstand. https://www.flickr.com/photos/oxfordian/11694201383/. [Online; accessed 23-April-2020].
[37]
Georgios Portokalidis and Angelos D. Keromytis. 2010. Fast and Practical Instruction-set Randomization for Commodity Systems. In Proceedings of the 26th Annual Computer Security Applications Conference (Austin, Texas, USA) (ACSAC '10). ACM, 41--48.
[38]
Achintya Prakash and Michael P. Wellman. 2015. Empirical Game-Theoretic Analysis for Moving Target Defense. In Proceedings of the Second ACM Workshop on Moving Target Defense (Denver, Colorado, USA) (MTD '15). ACM, 57--65.
[39]
M. Taguinod, A. Doupé, Z. Zhao, and G. J. Ahn. 2015. Toward a Moving Target Defense for Web Applications. In 2015 IEEE International Conference on Information Reuse and Integration. 510--517.
[40]
Joshua Taylor, Kara Zaffarano, Ben Koller, Charlie Bancroft, and Jason Syversen. 2016. Automated Effectiveness Evaluation of Moving Target Defenses: Metrics for Missions and Attacks. In Proceedings of the 2016 ACM Workshop on Moving Target Defense (Vienna, Austria) (MTD '16). ACM, 129--134.
[41]
PaX Team. 2001. PaX address space layout randomization (ASLR). (2001).
[42]
M. Thompson, N. Evans, and V. Kisekka. 2014. Multiple OS rotational environment an implemented Moving Target Defense. In 2014 7th International Symposium on Resilient Control Systems (ISRCS). 1--6.
[43]
Satya Gautam Vadlamudi, Sailik Sengupta, Marthony Taguinod, Ziming Zhao, Adam Doupé, Gail-Joon Ahn, and Subbarao Kambhampati. 2016. Moving Target Defense for Web Applications Using Bayesian Stackelberg Games: (Extended Abstract). In Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems (Singapore, Singapore) (AAMAS '16). International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 1377--1378. http://dl.acm.org/citation.cfm?id=2937029.2937168
[44]
Sridhar Venkatesan, Massimiliano Albanese, George Cybenko, and Sushil Jajodia. 2016. A Moving Target Defense Approach to Disrupting Stealthy Botnets. In Proceedings of the 2016 ACM Workshop on Moving Target Defense (Vienna, Austria) (MTD '16). ACM, 37--46.
[45]
S. Wang, H. Shi, Q. Hu, B. Lin, and X. Cheng. 2020. Moving Target Defense for Internet of Things Based on the Zero-Determinant Theory. IEEE Internet of Things Journal 7, 1 (Jan 2020), 661--668.
[46]
Bryan C Ward, Steven R Gomez, Richard Skowyra, David Bigelow, Jason Martin, James Landry, and Hamed Okhravi. 2018. Survey of Cyber Moving Targets Second Edition. Technical Report. MIT Lincoln Laboratory Lexington United States.
[47]
Samuel Woo, Daesung Moon, Taek-Young Youn, Yousik Lee, and Yongeun Kim. 2019. CAN ID shuffling technique (cist): Moving target defense strategy for protecting in-vehicle CAN. IEEE Access 7 (2019), 15521--15536.
[48]
B. Wu, Y. Ma, L. Fan, and F. Qian. 2018. Binary Software Randomization Method Based on LLVM. In 2018 IEEE International Conference of Safety Produce Informatization (IICSPI). 808--811.
[49]
X. Xiong, L. Yang, and G. Zhao. 2019. Effectiveness Evaluation Model of Moving Target Defense Based on System Attack Surface. IEEE Access 7 (2019), 9998--10014.
[50]
K. Zeitz, M. Cantrell, R. Marchany, and J. Tront. 2018. Changing the Game: A Micro Moving Target IPv6 Defense for the Internet of Things. IEEE Wireless Communications Letters 7, 4 (Aug 2018), 578--581.
[51]
Huan Zhang, Kangfeng Zheng, Xiaodan Yan, Shoushan Luo, and Bin Wu. 2020. Moving Target Defense Against Injection Attacks. In Algorithms and Architectures for Parallel Processing. Springer International Publishing, Cham, 518--532.
[52]
J. Zheng and A. Siami Namin. 2019. Enforcing Optimal Moving Target Defense Policies. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Vol. 1. 753--759.
[53]
Rui Zhuang, Scott A. DeLoach, and Xinming Ou. 2014. Towards a Theory of Moving Target Defense. In Proceedings of the First ACM Workshop on Moving Target Defense (Scottsdale, Arizona, USA) (MTD '14). ACM, 31--40.
[54]
Rui Zhuang, Su Zhang, Scott A. DeLoach, Xinming Ou, and Anoop Singhal. 2012. Simulation-based Approaches to Studying Effectiveness of Moving-Target Network Defense. In National Symposium on Moving Target Research (Annapolis, MD, USA). NIST. https://www.nist.gov/publications/simulation-based-approaches-studying-effectiveness-moving-target-network-defense

Cited By

View all
  • (2023)REORDER++: Enhanced Randomized Real-Time Scheduling Strategy Against Side-Channel AttacksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.3254653(1-14)Online publication date: 2023
  • (2021)Automated benchmark network diversification for realistic attack simulation with application to moving target defenseInternational Journal of Information Security10.1007/s10207-021-00552-921:2(253-278)Online publication date: 31-May-2021

Index Terms

  1. A critical view on moving target defense and its analogies

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        CF '20: Proceedings of the 17th ACM International Conference on Computing Frontiers
        May 2020
        298 pages
        ISBN:9781450379564
        DOI:10.1145/3387902
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Sponsors

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 23 May 2020

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. network security
        2. security and privacy
        3. software and application security
        4. system security

        Qualifiers

        • Research-article

        Conference

        CF '20
        Sponsor:
        CF '20: Computing Frontiers Conference
        May 11 - 13, 2020
        Sicily, Catania, Italy

        Acceptance Rates

        Overall Acceptance Rate 273 of 785 submissions, 35%

        Upcoming Conference

        CF '25

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)15
        • Downloads (Last 6 weeks)1
        Reflects downloads up to 05 Mar 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2023)REORDER++: Enhanced Randomized Real-Time Scheduling Strategy Against Side-Channel AttacksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.3254653(1-14)Online publication date: 2023
        • (2021)Automated benchmark network diversification for realistic attack simulation with application to moving target defenseInternational Journal of Information Security10.1007/s10207-021-00552-921:2(253-278)Online publication date: 31-May-2021

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media