[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

A Secure Lightweight Hardware-Assisted Charging Coordination Authentication Framework for Trusted Smart Grid Energy Storage Units

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Smart plug-in electrical vehicles (PEVs) have recently become essential components of the energy storage units (ESUs) in a smart power grid network. ESUs need to frequently communicate with charging stations for authentication before their battery systems are securely and efficiently charged. In this paper, an efficient lightweight hardware-assisted authentication and key management framework for ESU based charging coordination system is proposed. The framework integrates a hybrid lightweight arbiter linear feedback shift register (ALFSR) physical unclonable function and a low-cost advanced encryption standard (AES) for more secure, trusted, and robust secret key scheme. The scheme is implemented and validated on a reprogrammable device using 28 nm Field Programmable Gate Arrays (FPGA) platform. The results demonstrate that our framework can generate inherently unique and reliable secret keys. The proposed scheme is efficient in terms of key storage requirements and satisfies the authentication time of five security levels required by National Institute of Standards and Technology (NIST). Furthermore, the resilience of the proposed ALFSR is analyzed against ML modeling attacks, including k-nearest neighbor (kNN), kernel support vector machines (KernelSVM), and artificial neural network (ANN) which aim to clone the PUF behavior and compromise the secret key. The preliminary results demonstrate that the ALFSR PUF design is less vulnerable to kNN and SVM ML attacks as compared to ANN attacks.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /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 includes VAT (United Kingdom)

Instant access to the full article PDF.

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Baza M, Nabil M, Ismail M, Mahmoud M, Serpedin E, Ashiqur Rahman M. Blockchain-based charging coordination mechanism for smart grid energy storage units. In: 2019 IEEE International Conference on Blockchain (Blockchain). 2019; pp. 504–9.

  2. Brusco G, Burgio A, Menniti D, Pinnarelli A, Sorrentino N. Energy management system for an energy district with demand response availability. IEEE Trans Smart Grid. 2014;5(5):2385–93.

    Article  Google Scholar 

  3. Khurana H, Hadley M, Lu N, Frincke DA. Smart-grid security issues. IEEE Secur Privacy. 2010;8(1):81–5.

    Article  Google Scholar 

  4. Zhuang P, Zamir T, Liang H. Blockchain for cybersecurity in smart grid: a comprehensive survey. IEEE Trans Industr Inf. 2021;17(1):3–19.

    Article  Google Scholar 

  5. Quan H, Khosravi A, Yang D, Srinivasan D. A survey of computational intelligence techniques for wind power uncertainty quantification in smart grids. IEEE Trans Neural Networks Learn Syst. 2020;31(11):4582–99.

    Article  Google Scholar 

  6. Sortomme E, Hindi MM, MacPherson SDJ, Venkata SS. Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses. IEEE Trans Smart Grid. 2011;2(1):198–205.

    Article  Google Scholar 

  7. Wang M, Ismail M, Zhang R, Shen X, Serpedin E, Qaraqe K. Spatio-temporal coordinated V2V energy swapping strategy for mobile PEVs. IEEE Trans Smart Grid. 2018;9(3):1566–79.

    Article  Google Scholar 

  8. Mocera F, Vergori E, Somà A. Battery performance analysis for working vehicle applications. IEEE Trans Ind Appl. 2020;56(1):644–53.

    Article  Google Scholar 

  9. Lukic SM, Cao J, Bansal RC, Rodriguez F, Emadi A. Energy storage systems for automotive applications. IEEE Trans Ind Electron. 2008;55(6):2258–67.

    Article  Google Scholar 

  10. Khayyer P, Özgüner Ü. Decentralized control of large-scale storage-based renewable energy systems. IEEE Trans Smart Grid. 2014;5(3):1300–7.

    Article  Google Scholar 

  11. Wang J, Gharavi H. Power grid resilience [scanning the issue]. Proc IEEE. 2017;105(7):1199–201.

    Article  Google Scholar 

  12. Tesla motors-high performance electric vehicles. http://www.teslamotors.com. [Online; accessed 2021].

  13. Nissan LEAF electric car. http://www.nissanusa.com/leaf-electric-car. [Online; accessed 2021].

  14. Chevrolet, 2011 volt electric car. http://www.chevrolet.com/electriccar. [Online; accessed 2021].

  15. Challenges of EV charging. http://www.techrepublic.com/article/the-challenges-of-ev-charging-10-things-to-know/. [Online; accessed 2021].

  16. Ashok A, Govindarasu M, Wang J. Cyber-physical attack-resilient wide-area monitoring, protection, and control for the power grid. Proc IEEE. 2017;105(7):1389–407.

    Article  Google Scholar 

  17. Chung H, Li W, Yuen C, Chung W, Zhang Y, Wen C. Local cyber-physical attack for masking line outage and topology attack in smart grid. IEEE Trans Smart Grid. 2019;10(4):4577–88.

    Article  Google Scholar 

  18. Vellaithurai C, Srivastava A, Zonouz S, Berthier R. CPIndex: cyber-physical vulnerability assessment for power-grid infrastructures. IEEE Trans Smart Grid. 2015;6(2):566–75.

    Article  Google Scholar 

  19. Wang L, Khatamifard SK, Karpuzcu UR, Köse S. Exploring on-chip power delivery network induced analog covert channels. IEEE Trans Cyber-Phys Syst Newslett. 2019;4(1):15–8.

    Google Scholar 

  20. Khatamifard SK, Wang L, Köse S, Karpuzcu UR. POWERT channels: a novel class of covert communication exploiting power management vulnerabilities. In: Proceedings of the IEEE international symposium on high-performance computer architecture (HPCA), 2019. pp. 291–303.

  21. Khatamifard SK, Wang L, Köse S, Karpuzcu UR. A new class of covert channels exploiting power management vulnerabilities. IEEE Comput Archit Lett. 2018;17(2):201–4.

    Article  Google Scholar 

  22. Gope P, Sikdar B. Lightweight and privacy-preserving two-factor authentication scheme for IoT devices. IEEE Internet Things J. 2019;6(1):580–9.

    Article  Google Scholar 

  23. Yanambaka VP, Mohanty SP, Kougianos E, Puthal D. PMsec: physical unclonable function-based robust and lightweight authentication in the internet of medical things. IEEE Trans Consum Electron. 2019;65(3):388–97.

    Article  Google Scholar 

  24. Zhao B, Zhao P, Fan P. ePUF: a lightweight double identity verification in IoT. Tsinghua Sci Technol. 2020;25(5):625–35.

    Article  Google Scholar 

  25. Herder C, Yu M, Koushanfar F, Devadas S. Physical unclonable functions and applications: a tutorial. Proc IEEE. 2014;102(8):1126–41.

    Article  Google Scholar 

  26. Rührmair U, et al. PUF modeling attacks on simulated and silicon data. IEEE Trans Inf Forensics Secur. 2013;8(11):1876–91.

    Article  Google Scholar 

  27. Suh GE, Devadas S. Physical unclonable functions for device authentication and secret key generation. In: 2007 44th ACM/IEEE Design Automation Conference. CA: San Diego; 2007.

  28. Lee W, Lim Daihyun, Gassendi B, van Suh GE, Dijk M, Devadas S. A technique to build a secret key in integrated circuits for identification and authentication applications. In: 2004 Symposium on VLSI Circuits. Digest of Technical Papers (IEEE Cat. No.04CH37525), Honolulu, HI, USA, 2004.

  29. Azhar MJ, Amsaad F, Kose S. Duty-cycle-based controlled physical unclonable function. IEEE Trans Very Large Scale Integr Syst. 2018;26(9):1647–58.

    Article  Google Scholar 

  30. Pundir N, Amsaad F, Choudhury M, Niamat M. Novel technique to improve strength of weak arbiter PUF. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). Boston MA; 2017.

  31. Maiti A, Casarona J, McHale L, Schaumont P. A large scale characterization of RO-PUF. In: 2010 IEEE international symposium on hardware-oriented security and trust (HOST). CA: Anaheim; 2010.

  32. Amsaad F, Razaque A, Baza M, Köse S, Bhatia S, Srivastava G. An efficient and reliable lightweight PUF for IoT-based applications. In: 2021 IEEE international conference on communications workshops (ICC Workshops), 2021.

  33. Amsaad F, Köse S. A secure hardware-assisted AMI authentication scheme for smart cities. IEEE Consum Electron Mag. 2021;10(4):106–12. https://doi.org/10.1109/MCE.2020.3040717.

  34. Amsaad F, Köse S. Trusted authentication scheme A, for IoT-based smart grid applications. In: IEEE 6th world forum on Internet of Things (WF-IoT). New Orleans, LA, USA. 2020;2020:1–6.

  35. Chatterjee B, Das D, Maity S, Sen S. RF-PUF: enhancing IoT security through authentication of wireless nodes using in-situ machine learning. IEEE Internet Things J. 2019;6(1):388–98.

    Article  Google Scholar 

  36. Sengupta A, Kundu S. Guest editorial securing IoT hardware: threat models and reliable, low-power design solutions. IEEE Trans Very Large Scale Integr Syst. 2017;25(12):3265–7.

    Article  Google Scholar 

  37. Dong C, He G, Liu X, Yang Y, Guo W. A multi-layer hardware trojan protection framework for IoT chips. IEEE Access. 2019;7:23628–39.

    Article  Google Scholar 

  38. Guan Z, Liu H, Qin Y. Physical unclonable functions for IoT device authentication. J Commun Inf Netw. 2019;4(4):44–54.

    Article  Google Scholar 

  39. Larimian S, Mahmoodi MR, Strukov DB. Lightweight integrated design of PUF and TRNG security primitives based on eFlash memory in 55-nm CMOS. IEEE Trans Electron Devices. 2020;67(4):1586–92.

    Article  Google Scholar 

  40. Zhang J, Qu G. Physical unclonable function-based key sharing via machine learning for IoT security. IEEE Trans Ind Electron. 2020;67(8):7025–33.

    Article  Google Scholar 

  41. Chen B, Willems FMJ. Secret key generation over biased physical unclonable functions with polar codes. IEEE Internet Things J. 2019;6(1):435–45.

    Article  Google Scholar 

  42. Amsaad F, Sherif A, Dawoud A, Niamat M, Köse S. A Novel FPGA-based LFSR PUF design for IoT and smart applications. In: NAECON, IEEE National Aerospace and Electronics Conference. Dayton, OH. 2018;2018:99–104.

  43. Nath APD, Amsaad F, Choudhury M, Niamat M. Hardware-based novel authentication scheme for advanced metering infrastructure. In: IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS). Dayton, OH. 2016;2016:364–71.

  44. Shi J, Lu Y, Zhang J. Approximation attacks on strong PUFs. IEEE Trans Comput Aided Des Integr Circ Syst. 2020;39(10):2138–51.

    Article  Google Scholar 

  45. Wold K, Tan CH. Analysis and enhancement of random number generator in FPGA based on oscillator rings. Int Conf Reconfigur Comput FPGAs. 2008;2008:385–90.

    Article  Google Scholar 

  46. Comer JM, Cerda JC, Martinez CD, Hoe DHK. Random number generators using Cellular Automata implemented on FPGAs. In: Proceedings of the 2012 44th Southeastern Symposium on System Theory (SSST), 2012; pp. 67–72.

  47. Tuncer T, Avaroglu E, Turk M, Bedri Ozer A. Implementation of the non-periodic sampling true random number generator on FPGA. J Microelectron Elec Component Mater. 2014;44(4):296–302.

  48. Kumar R, Burleson W. PHAP: Password based hardware authentication using PUFs. In: 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture Workshops, 2012; pp. 24–31.

  49. Kulseng L, Yu Z, Wei Y, Guan Y. Lightweight mutual authentication and ownership transfer for RFID systems. Proc IEEE INFOCOM. 2010;2010:1–5.

    Google Scholar 

  50. Hori Y, Kang H, Katashita T, Satoh A. Pseudo-LFSR PUF: a compact, efficient and reliable physical unclonable function. Int Conf Reconfig Comput FPGAs. 2011;2011:223–8.

    Google Scholar 

  51. Smith Tony C, Frank Eibe. Statistical genomics: methods and protocols, chapter introducing machine learning concepts with WEKS. New York: Springer; 2016. p. 353–78.

    Book  Google Scholar 

  52. Caruana R, Niculescu-Mizil A. An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd International Conference on Machine Learning. 2006. pp. 161–8.

  53. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157–82.

  54. Ruhrmair U, Sehnke F, Solter J, Dror G, Devadas S, Schmidhuber J. Modeling attacks on physical unclonable functions. In: Proceedings of the 17th ACM conference on Computer and communications security. ACM, 2010;pp. 237–49.

  55. Ruhrmair U, Solter J, Sehnke F, Xu X, Mahmoud A, Stoyanova V, Dror G, Schmidhuber J, Burleson W, Devadas S. PUF modeling attacks on simulated and silicon data. IEEE Trans Inf Forensics Secur. 2013;8(11):1876–91.

    Article  Google Scholar 

  56. Kocher P, Jaffe J, Jun B. Differential power analysis. In: Annual International Cryptology Conference. Springer. 1999;388–97.

  57. Kocher P.C. (1996) Timing Attacks on Implementations of Diffie-Hellman, RSA, DSS, and Other Systems. In: Koblitz N. (eds) Advances in Cryptology — CRYPTO ’96. CRYPTO 1996. Lecture Notes in Computer Science, vol 1109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-68697-5_9.

  58. Merli D, Schuster D, Stumpf F, Sigl G. Semi-invasive EM attack on FPGA RO PUFs and countermeasures. In: Proceedings of the Workshop on Embedded Systems Security, 2011;pp. 1–9.

  59. Delvaux J, Verbauwhede I. Side-channel modeling attacks on 65nm arbiter PUFs exploiting CMOS device noise. IEEE Int Sympos Hardware-Orient Secur Trust. 2013;2013:137–42.

    Google Scholar 

  60. Tajik S, Dietz E, Frohmann S, Dittrich H, Nedospasov D, Helfmeier C, Seifert J-P, Boit C, Hubers H-W. Photonic side-channel analysis of arbiter PUFs. J Cryptol. 2017;30(2):550–71.

    Article  MATH  Google Scholar 

  61. Mahmoud A, Rhrmair U, Majzoobi M, Koushanfar F. Combined modeling and side-channel attacks on strong PUFs. IACR Cryptol ePrint Arch. 2013;Oct;2013:632.

  62. Lim D, Lee JW, Gassendi B, Suh GE, van Dijk M, Devadas S. Extracting secret keys from integrated circuits. IEEE Trans Very Large Scale Integr Syst. 2005;13(10):1200–5.

    Article  Google Scholar 

  63. Amsaad F, Chaudhuri CR, Niamat M. Reliable and reproducible PUF based cryptographic keys under varying environmental conditions. In: IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS), 2016;pp. 468–73.

  64. Amsaad F, Sherif A, Dawoud A, Niamat M, Köse S. A novel FPGA-based LFSR PUF design for IoT and smart applications. In: IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS), 2018;pp. 99–104.

  65. Amsaad F, Prasad A, Roychaudhuri C, Niamat M. A novel security technique to generate truly random and highly reliable reconfigurable ROPUF-based cryptographic keys. In: Proceedings of the IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2016;pp. 185–90.

  66. Amsaad F, Pundir N, Niamat MA. Dynamic area-efficient technique to enhance ROPUFs security against modeling attacks. In: Computer and network security essentials Springer International Publishing. 2018. p. 407–25.

  67. Azhar MJ, Amsaad F, Köse S. Duty-cycle-based controlled physical unclonable function. IEEE Trans Very Large Scale Integr Syst. 2018;26(9):1647–58.

    Article  Google Scholar 

  68. Azhar M, Köse S. Process, voltage, and temperature-stable adaptive duty cycle based PUF. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), 2018;pp. 1–5.

  69. Yu W, Chen Y, Köse S, Chen J. Exploiting multi-phase on-chip voltage regulators as strong PUF primitives for securing IoT. J Electron Test. 2018;34(5):587–98.

    Article  Google Scholar 

  70. The Smart grid Interoperability Panel - Cyber Security Working Group. Guidelines for smart grid cybersecurity. NISTIR. 2010;7628:1–597.

    Google Scholar 

  71. Office of the National Coordinator for Smart grid Interoperability, NIST framework and roadmap for Smart grid interoperability standards, release 1.0, NIST Special. Publication. 2010;1108:1–145.

  72. Wu P, Fang C, Chang JM, Kung S. Cost-effective kernel ridge regression implementation for Keystroke-based active authentication system. IEEE Trans Cybernet. 2017;47(11):3916–27.

    Article  Google Scholar 

  73. The Advanced Encryption Standard https://docplayer.net/14171143-The-advanced-encryption-standard-aes.html. Accessed 2021.

  74. Amsaad F, Niamat M, Dawoud A, Kose S. Reliable delay based algorithm to boost PUF security against modeling attacks. Information. 2018;9(9):1–15.

    Article  Google Scholar 

  75. Simple guide to Confusion Matrix Terminology https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/. Accessed 2021.

Download references

Acknowledgements

This work is supported in part by the NSF Award under Grant CNS-1929774.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Selçuk Köse.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

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

This article is part of the topical collection “Technologies and Components for Smart Cities” guest edited by Himanshu Thapliyal, Saraju P. Mohanty, Srinivas Katkoori and Kailash Chandra Ray.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amsaad, F., Köse, S. A Secure Lightweight Hardware-Assisted Charging Coordination Authentication Framework for Trusted Smart Grid Energy Storage Units. SN COMPUT. SCI. 2, 444 (2021). https://doi.org/10.1007/s42979-021-00840-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00840-0

Keywords

Navigation