[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Triple methods-based empirical assessment of the effectiveness of adaptive cyber defenses in the cloud

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The flexible and cost-effective service provided by cloud computing has led to the development of a vast array of applications in smart cities. Nevertheless, their traditional security approaches presented the concept of a static target for attacks, leading to an asymmetric situation between defenders and attackers. Adaptive cyber defense (ACD) has, therefore, recently emerged as a game-changer to reverse this asymmetry by reconfiguring the system according to the network scenario. Analyzing and quantifying the effectiveness of these ACDs are of high importance. Previous research on ACD analysis focused more on either studying the system properties using experiment-based approaches or on evaluating its effectiveness by different mathematical modeling approaches. However, little effort has been made to overcome the problems of isolated solutions. In this paper, we described the defensive process as a racing game between the attacker and the defender. On this basis, we conducted a thorough ACD effectiveness evaluation and suggested a comparison strategy using three methods, namely semi-Markov, stochastic reward net, and experimental methods. The obtained simulation results were approximate, and the error rate was less than 3.36% reflecting the reliability of the proposed methods. Based on the assessments, we finally summarized the features of these methods to specify and deduce different scenarios and their corresponding suitable evaluation method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Availability of data and materials

Data and materials are available on request from the authors.

References

  1. Manadhata PK, Wing JM (2010) An attack surface metric. IEEE Trans Softw Eng 3:371–386

    Google Scholar 

  2. Jajodia S, Ghosh AK, Swarup V, Wang C, Wang XS (2011) Moving Target Defense: Creating Asymmetric Uncertainty for Cyber Threats, vol 54. Springer, New York

    Book  Google Scholar 

  3. Albanese M, Connell W, Venkatesan S, Cybenko G (2019) Moving target defense quantification. In: Proceedings of Adversarial and Uncertain Reasoning for Adaptive Cyber Defense, pp 94–111. Springer

  4. Li G, Wang W, Gai K, Tang Y, Si X (2021) A framework for mimic defense system in cyberspace. J Signal Process Syst, pp 169–185

  5. Connell W, Menasce DA, Albanese M (2018) Performance modeling of moving target defenses with reconfiguration limits. IEEE Trans Dependable Secure Comput 18(1):205–219

    Article  Google Scholar 

  6. Sianipar J, Sukmana M, Meinel C (2018) Moving sensitive data against live memory dumping, spectre and meltdown attacks. In: Proceedings of the 26th International Conference on Systems Engineering (ICSEng), IEEE, pp 1–8

  7. Zhou Y, Cheng G, Jiang S, Zhao Y, Chen Z (2020) Cost-effective moving target defense against ddos attacks using trilateral game and multi-objective Markov decision processes. Comput Secur 97:1–12

    Article  Google Scholar 

  8. Xing L, Levitin G, Xiang Y (2019) Defending n-version programming service components against co-resident attacks in iot cloud systems. IEEE Trans Services Comput, pp 1–9 . https://doi.org/10.1109/TSC.2019.2904958

  9. Albanese M, Jajodia S, Venkatesan S (2018) Defending from stealthy botnets using moving target defenses. IEEE Security Privacy 16(1):92–97. https://doi.org/10.1109/MSP.2018.1331034

    Article  Google Scholar 

  10. Lei C, Zhang H-Q, Tan J-L, Zhang Y-C, Liu X-H (2018) Moving target defense techniques: a survey. Secur Commun Netw 2018:163–177

    Article  Google Scholar 

  11. Ross SM, Kelly JJ, Sullivan RJ et al (1983) Stochastic processes. Wiley, New York

    Google Scholar 

  12. Chiola G, Marsan MA, Balbo G, Conte G (1993) Generalized stochastic petri nets: a definition at the net level and its implications. IEEE Trans Softw Eng 19(2):89–107

    Article  Google Scholar 

  13. Hong JB, Kim DS (2016) Assessing the effectiveness of moving target defenses using security models. IEEE Trans Dependable Secure Comput 13(2):163–177

    Article  Google Scholar 

  14. Yang X, Li H, Wang H (2018) Npm: an anti-attacking analysis model of the mtd system based on martingale theory. In: Proceedings of IEEE Symposium on Computers and Communications (ISCC), IEEE, pp 566–572

  15. Yang X, Li H, Wu J, Yi P (2020) A two-dimension security assessing model for CMDs combined with generalized stochastic petri net. Sci Sin Inform 50(12):166–182

    Google Scholar 

  16. Trivedi KS, Bobbio A (2017) Reliability and availability engineering, vol 10.1017/9781316163047. Cambridge University Press, North Carolina

  17. Levitin G, Xing L, Xiang Y (2020) Reliability vs. vulnerability of n-version programming cloud service component with dynamic decision time under co-resident attacks. IEEE Trans Serv Comput, pp 1–12

  18. Levitin G, Xing L, Xiang Y (2022) Co-residence data theft attacks on n-version programming-based cloud services with task cancelation. IEEE Trans Syst Man Cybern: Syst 52(1):324–333. https://doi.org/10.1109/TSMC.2020.3002930

    Article  Google Scholar 

  19. Chang X, Shi Y, Zhang Z, Xu Z, Trivedi K (2020) Job completion time under migration-based dynamic platform technique. IEEE Trans Serv Comput, 1–13. https://doi.org/10.1109/TSC.2020.2989215

  20. Torquato M, Maciel P, Vieira M (2021) Analysis of vm migration scheduling as moving target defense against insider attacks. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing, pp 194–202

  21. Nguyen M, Samanta P, Debroy S (2018) Analyzing moving target defense for resilient campus private cloud. In: Proceedings of the 11th International Conference on Cloud Computing (CLOUD), IEEE, pp 114–121

  22. Zhuang R, Zhang S, Deloach S.A, Ou X, Singhal A (2013) Simulation-based approaches to studying effectiveness of moving target network defense. In: Proceedings of National Symposium on Moving Target Research, ACM, pp 15111–15126

  23. Jin H, Li Z, Zou D, Yuan B (2019) Dseom: a framework for dynamic security evaluation and optimization of mtd in container-based cloud. IEEE Trans Depend Secure Comput 18(3):1125–1136

    Google Scholar 

  24. Yang C, Guo Y, Hu H, Liu W, Wang Y (2019) An effective and scalable vm migration strategy to mitigate cross-vm side-channel attacks in cloud. China Commun 16(4):151–171

    Google Scholar 

  25. Azab M, Eltoweissy M (2016) Migrate: towards a lightweight moving-target defense against cloud side-channels. In: Proceedings of IEEE Security and Privacy Workshops (SPW), IEEE, pp 96–103

  26. Ren Q, Hu T, Wu J, Hu Y, He L, Lan J (2021) Multipath resilient routing for endogenous secure software defined networks. Comput Netw 194:108134. https://doi.org/10.1016/j.comnet.2021.108134

    Article  Google Scholar 

  27. Anderson N, Mitchell R, Chen I-R (2016) Parameterizing moving target defenses. In: Proceedings of IFIP International Conference on New Technologies, IEEE, pp 1–6

  28. Mitchell R, Chen IR (2015) Modeling and analysis of attacks and counter defense mechanisms for cyber physical systems. IEEE Trans Reliab 65(1):350–358

    Article  Google Scholar 

  29. Cai G, Wang B, Luo Y, Hu W (2016) A model for evaluating and comparing moving target defense techniques based on generalized stochastic petri net. In: Proceedings of Advanced Computer Architecture, Springer, pp 184–197

  30. Wu J (2017) Introduction to Cyberspace Mimic Defense. Science Press, Beijing

    Google Scholar 

  31. Torquato M, Maciel P, Vieira M (2020) Availability and reliability modeling of vm migration as rejuvenation on a system under varying workload. Softw Qual J 28(1):59–83

    Article  Google Scholar 

  32. Chen Z, Chang X, Han Z, Yang Y (2020) Numerical evaluation of job finish time under mtd environment. IEEE Access 8:11437–11446

    Article  Google Scholar 

  33. Prakash A, Wellman MP (2015) Empirical game-theoretic analysis for moving target defense. In: Proceedings of the Second ACM Workshop on Moving Target Defense, ACM, pp 57–65

  34. Sengupta S, Vadlamudi S.G, Kambhampati S (2017) A game theoretic approach to strategy generation for moving target defense in web applications. In: Proceedings of International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

  35. Eldosouky A.R, Saad W, Niyato D (2016) Single controller stochastic games for optimized moving target defense. In: Proceedings of IEEE International Conference on Communications (ICC), IEEE, pp 1–6

  36. Tan J, Lei C, Zhang H, Cheng Y (2019) Optimal strategy selection approach to moving target defense based on markov robust game. Comput Secur 85:63–76

    Article  Google Scholar 

  37. Maleki H, Valizadeh S, Koch W, Bestavros A, Dijk M.V (2016) Markov modeling of moving target defense games. In: Proceedings of ACM Workshop on Moving Target Defense, ACM, pp 81–92

  38. Debroy S, Calyam P, Nguyen M, Stage A, Georgiev V (2016) Frequency-minimal moving target defense using software-defined networking. In: Proceedings of International Conference on Computing, IEEE, pp 1–6

  39. Han Y, Chan J, Alpcan T, Leckie C (2017) Using virtual machine allocation policies to defend against co-resident attacks in cloud computing. IEEE Trans Dependable Secure Comput 14(1):95–108. https://doi.org/10.1109/TDSC.2015.2429132

    Article  Google Scholar 

  40. Zhuang R, Zhang S, Bardas A, Deloach S.A, Ou X, Singhal A (2013) Investigating the application of moving target defenses to network security. In: Proceedings of International Symposium on Resilient Control Systems, IEEE, pp 162–169

  41. Zhuang R, DeLoach S.A, Ou X (2014) A model for analyzing the effect of moving target defenses on enterprise networks. In: Proceedings of Annual Cyber and Information Security Research Conference (CISR), ACM, pp 73–76

  42. Lei C, Zhang H, Wan L, Liu L, Ma D (2018) Incomplete information markov game theoretic approach to strategy generation for moving target defense. Comput Commun 116:184–199

    Article  Google Scholar 

  43. Jajodia S, Park N, Serra E, Subrahmanian VS (2018) Share: a stackelberg honey-based adversarial reasoning engine. ACM Trans Internet Technol 18(3):1–41

    Article  Google Scholar 

  44. Ciardo G, Muppala J, Trivedi T (1989) Spnp: stochastic petri net package. In: Proceedings of International Workshop on Petri Nets and Performance Models, pp 142–151

Download references

Acknowledgments

This work was supported by the Guangdong Province Research and Development Key Program [grant number 2019B010137001]; Basic Research Enhancement Program of China (Grant number 2021-JCJQ-JJ-0483); Shenzhen Research Programs (Grant numbers GXWD20201231165807007-20200807164903001; JCYJ20210324122013036; JCYJ20190808155607340); ZTE Funding (Grant number 2019ZTE03-01).

Funding

Guangdong Province Research and Development Key Program (Grant number 2019B010137001); Basic Research Enhancement Program of China (Grant number 2021-JCJQ-JJ-0483); Shenzhen Research Programs (Grant Number GXWD20201231165807007-20200807164903001; JCYJ20210324122013036; JCYJ20190808155607340); ZTE Funding (Grant Number 2019ZTE03-01).

Author information

Authors and Affiliations

  1. School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, Guangdong, China

    Xin Yang, Abla Smahi & Hui Li

  2. Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China

    Hui Li

  3. Zhongxing Telecom Equipment (ZTE) Corporation, Shenzhen, 518057, Guangdong, China

    Ping Lu

  4. Purple Mountain Laboratories, Nanjing, 211111, Jiangsu, China

    Huayu Zhang

  5. University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China

    Shuo-Yen Robert Li

Authors
  1. Xin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abla Smahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ping Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huayu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shuo-Yen Robert Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Xin Yang contributed to concept conceptualization, formal analysis, investigation, methodology, preparing figures, writing—original draft. Abla Smahi contributed to investigation, validation, visualization, writing—review & editing. Hui Li contributed to funding acquisition, project administration, resources, supervision, writing—review & editing. Ping Lu contributed to funding acquisition, resources. Huayu Zhang contributed to validation, visualization, writing—review & editing. Shuo-Yen Robert Li contributed to supervision, validation, writing—review & editing. Provide the same order of author in both the system and the manuscript file and the meta-data.

Corresponding author

Correspondence to Hui Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Consent for publication

Not applicable.

Ethics approval

No participation of humans takes place in this implementation process.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, X., Smahi, A., Li, H. et al. Triple methods-based empirical assessment of the effectiveness of adaptive cyber defenses in the cloud. J Supercomput 79, 8634–8667 (2023). https://doi.org/10.1007/s11227-022-04984-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04984-5

Keywords