Skip to main content

Advertisement

Springer Nature Link
Log in

Exploring the potential of deep learning and machine learning techniques for randomness analysis to enhance security on IoT

  • Regular Contribution
  • Published:
International Journal of Information Security Aims and scope Submit manuscript

Abstract

The Internet of Things (IoT) is an incredibly growing technology. However, due to hardware inadequacy, IoT security is not improving to the same extent. For this reason, lightweight encryption algorithms have begun to be developed. This paper presents a method for assessing the security of Pseudorandom Number Generator (PRNG) generated binary sequences in a reasonable time using a pre-trained deep learning (DL) model. Due to their long execution times, Randomness Test Standards (RTSs) that include statistical tests that examine whether the sequences generated by PRNGs contain any patterns that cause cryptographic vulnerabilities are not suitable for running on edge devices with low processing capacities such as the IoT. We argue that every random sequence, even generated by a PRNGs that are classified as cryptographically secure, utilized in cryptographic applications should be used after successful results obtained from RTSs in every time. Therefore, an alternative method based on machine learning has been proposed to overcome the processing time problem of these test suites. The most utilized RTSs are NIST 800-22 Rev.1a, GB/T 32915-2016 and AIS 20/31. The 800-22 Rev.1a, which NIST has designated as a standard, has been observed to be the most referenced test standard in the literature. With this implementation, we sought to show that 15 statistical tests of the NIST 800-22 rev.1a environment can be modeled using DL. The application findings indicate that this modeling can serve as an alternative to the existing test environments. The average accuracy recorded throughout 15 tests was 98.64 percent. As a result, the trained model can be implemented even in edge computing devices with limited capability including IoTs.

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

Similar content being viewed by others

Data availability

Generated dataset will be published as open source for researchers. Also, will be available to those requests.

References

  1. Fritzmann, T., Vith, J., Flórez, D., Sepúlveda, J.: Post-quantum cryptography for automotive systems. Microprocess. Microsyst. 87, 104379 (2021). https://doi.org/10.1016/j.micpro.2021.104379

    Article  Google Scholar 

  2. Mcginthy, J.M., Michaels, A.J.: Further analysis of PRNG-based key derivation functions. IEEE Access 7, 95978–95986 (2019). https://doi.org/10.1109/ACCESS.2019.2928768

    Article  Google Scholar 

  3. Namasudra, S.: A secure cryptosystem using DNA cryptography and DNA steganography for the cloud-based IoT infrastructure. Comput. Electr. Eng. 1(104), 108426 (2022)

    Article  Google Scholar 

  4. Timo, B.: Random numbers. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/816507

  5. Weinberger, K., Dasgupta, A., Langford, J., Smola, A., Attenberg, J.: Feature hashing for large scale multitask learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, in ICML ’09, pp. 1113–1120. Association for Computing Machinery, New York, NY (2009). https://doi.org/10.1145/1553374.1553516.

  6. Jang, J., Brumley, D., Venkataraman, S.: Bitshred: feature hashing malware for scalable triage and semantic analysis. In: Proceedings of the 18th ACM Conference on Computer and Communications Security (2011). https://doi.org/10.1145/2046707.2046742. Accessed 31 Jan 2023

  7. Zhang, Z., Qi, P., Wang, W.: Dynamic malware analysis with feature engineering and feature learning. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, pp. 1210–1217. AAAI Press, New York (2020). https://ojs.aaai.org/index.php/AAAI/article/view/5474. Accessed 31 Jan 2023

  8. Sharma, P., Singh, A.: Era of deep neural networks: a review. In: 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–5 (2017). https://doi.org/10.1109/ICCCNT.2017.8203938

  9. Patgiri, R., Biswas, A., Nayak, S.: deepBF: malicious URL detection using learned bloom filter and evolutionary deep learning. Comput. Commun. 200, 30–41 (2023). https://doi.org/10.1016/j.comcom.2022.12.027

    Article  Google Scholar 

  10. Panwar, K., Kukreja, S., Singh, A., Singh, K.K.: Towards deep learning for efficient image encryption. Procedia Comput. Sci. 218, 644–650 (2023). https://doi.org/10.1016/j.procs.2023.01.046

    Article  Google Scholar 

  11. Sun, C.-Y., Wu, A.C.-H., Hwang, T.: A novel privacy-preserving deep learning scheme without a cryptography component. Comput. Electr. Eng. 94, 107325 (2021). https://doi.org/10.1016/j.compeleceng.2021.107325

    Article  Google Scholar 

  12. Zhuang, X., Yan, A.: Deep-learning-based ciphertext-only attack on optical scanning cryptosystem. Opt. Laser Technol. 157, 108744 (2023). https://doi.org/10.1016/j.optlastec.2022.108744

    Article  Google Scholar 

  13. Hung, H.-N., Lee, P.-C., Lin, Y.-B.: Random number generation for excess life of mobile user residence time. IEEE Trans. Veh. Technol. 55(3), 1045–1050 (2006). https://doi.org/10.1109/TVT.2006.874578

    Article  Google Scholar 

  14. Uchida, K., Tanamoto, T., Fujita, S.: Single-electron random-number generator (RNG) for highly secure ubiquitous computing applications. Solid-State Electron. 51(11–12), 1552–1557 (2007). https://doi.org/10.1016/j.sse.2007.09.015

    Article  Google Scholar 

  15. Miyabe, K., Takemura, A.: Convergence of random series and the rate of convergence of the strong law of large numbers in game-theoretic probability. Stoch. Process. Their Appl. 122, 1–30 (2012)

    Article  MathSciNet  Google Scholar 

  16. Kamada, M.: A network game based on fair random numbers. IEICE Trans. Inf. Syst. E88-D(5), 859–864 (2005). https://doi.org/10.1093/ietisy/e88-d.5.859

    Article  Google Scholar 

  17. Boland, P.J., Pawitan, Y.: Trying to be random in selecting numbers for lotto. J. Stat. Educ. (1999). https://doi.org/10.1080/10691898.1999.12131278

    Article  Google Scholar 

  18. Fazili, M.M., Shah, M.F., Naz, S.F., Shah, A.P.: Next generation QCA technology based true random number generator for cryptographic applications. Microelectron. J. 126, 105502 (2022)

    Article  Google Scholar 

  19. Morsali, M., Moaiyeri, M.H., Rajaei, R.: A process variation resilient spintronic true random number generator for highly reliable hardware security applications. Microelectron. J. 129, 105606 (2022)

    Article  Google Scholar 

  20. Fan, F., Wang, G.: Learning from pseudo-randomness with an artificial neural network-does god play pseudo-dice? IEEE Access 6, 22987–22992 (2018). https://doi.org/10.1109/ACCESS.2018.2826448

    Article  Google Scholar 

  21. Wang, C., Zhang, Y.: A novel image encryption algorithm with deep neural network. Signal Process. (2022). https://doi.org/10.1016/j.sigpro.2022.108536

    Article  Google Scholar 

  22. Almaraz Luengo, E., Leiva Cerna, M.B., García Villalba, L.J., Hernandez-Castro, J.: A new approach to analyze the independence of statistical tests of randomness. Appl. Math. Comput. 426, 127116 (2022). https://doi.org/10.1016/j.amc.2022.127116

    Article  Google Scholar 

  23. Wang, X., Liu, L., Zhang, Y.: A novel chaotic block image encryption algorithm based on dynamic random growth technique. Opt. Lasers Eng. 66, 10–18 (2015)

    Article  Google Scholar 

  24. Liu, H., Wang, X.: Color image encryption using spatial bit-level permutation and high-dimension chaotic system. Opt. Commun. 284(16–17), 3895–3903 (2011)

    Article  Google Scholar 

  25. Cheon, J.H., Kim, J.: A hybrid scheme of public-key encryption and somewhat homomorphic encryption. IEEE Trans. Inf. Forensics Secur. 10(5), 1052–1063 (2015). https://doi.org/10.1109/TIFS.2015.2398359

    Article  Google Scholar 

  26. Zhang, Y., Monteiro, D., Liang, H.-N., Ma, J., Baghaei, N.: Effect of input-output randomness on gameplay satisfaction in collectable card games. In: 2021 IEEE Conference on Games (CoG), pp. 01–05 (2021). https://doi.org/10.1109/CoG52621.2021.9619020

  27. Mahapatra, D.P., Triambak, S.: Towards predicting COVID-19 infection waves: a random-walk Monte Carlo simulation approach. Chaos Solitons Fractals 156, 111785 (2022). https://doi.org/10.1016/j.chaos.2021.111785

    Article  Google Scholar 

  28. Novikov, A., Kuzmin, D., Ahmadi, O.: Random walk methods for Monte Carlo simulations of Brownian diffusion on a sphere. Appl. Math. Comput. 364, 124670 (2020). https://doi.org/10.1016/j.amc.2019.124670

    Article  MathSciNet  Google Scholar 

  29. Serrano, R., et al.: A fully digital true random number generator with entropy source based in frequency collapse. IEEE Access 9, 105748–105755 (2021). https://doi.org/10.1109/ACCESS.2021.3099534

    Article  Google Scholar 

  30. Petrie, C.S., Connelly, J.A.: A noise-based IC random number generator for applications in cryptography. IEEE Trans. Circuits Syst. Fundam. Theory Appl. 47(5), 615–621 (2000). https://doi.org/10.1109/81.847868

    Article  Google Scholar 

  31. Stipčević, M.: Quantum random number generators and their use in cryptography. In: 2011 Proceedings of the 34th international convention MIPRO, pp. 1474–1479 (2011_.

  32. Thornton, M.A., MacFarlane, D.L.: Quantum photonic TRNG with dual extractor. In: Quantum Technology and Optimization Problems, Feld, S., Linnhoff-Popien, C. (eds.), in Lecture Notes in Computer Science, pp. 171–182. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-14082-3_15

  33. Rohe, M.: RANDy—a true-random generator based on radioactive decay, p. 36

  34. Laurenciu, N.C., Cotofana, S.D.: Low cost and energy, thermal noise driven, probability modulated random number generator. In: 2015 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2724–2727 (2015). https://doi.org/10.1109/ISCAS.2015.7169249

  35. Yao, Y., Chen, X., Kang, W., Zhang, Y., Zhao, W.: Thermal brownian motion of Skyrmion for true random number generation. IEEE Trans. Electron Devices 67(6), 2553–2558 (2020). https://doi.org/10.1109/TED.2020.2989420

    Article  Google Scholar 

  36. Tariq, N., Khan, F.A., Asim, M.: Security challenges and requirements for smart internet of things applications: a comprehensive analysis. Procedia Comput. Sci. 191, 425–430 (2021). https://doi.org/10.1016/j.procs.2021.07.053

    Article  Google Scholar 

  37. Machicao, J., Ngo, Q.Q., Molchanov, V., Linsen, L., Bruno, O.: A visual analysis method of randomness for classifying and ranking pseudo-random number generators. Inf. Sci. 558, 1–20 (2021)

    Article  MathSciNet  Google Scholar 

  38. Hegadi, R., Patil, A.P.: A statistical analysis on in-built pseudo random number generators using NIST test suite. In: 2020 5th international conference on computing, communication and security (ICCCS), pp. 1–6 (2020). https://doi.org/10.1109/ICCCS49678.2020.9276849

  39. von Neumann, J.: Various techniques used in connection with random digits. Natl. Bur. Stand. Appl. Math. Ser. 12, 36–38 (1951)

    Google Scholar 

  40. Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8(1), 3–30 (1998). https://doi.org/10.1145/272991.272995

    Article  Google Scholar 

  41. Bassham, L.E. et al.: SP 800–22 Rev. 1a. A statistical test suite for random and pseudorandom number generators for cryptographic applications. National Institute of Standards & Technology, Gaithersburg, Technical Report (2010)

  42. Killmann, W., Schindler, W.: A proposal for functionality classes for random number generators (2011)

  43. Dawei, L., Dengguo, F., Hua, C.: Information security technology binary sequence randomness detection metod. China National Standardization Administration, GB/T 32915-2016 (2016)

  44. Mengdi, Z., Xiaojuan, Z., Yayun, Z., Siwei, M.: Overview of randomness test on cryptographic algorithms. J. Phys. Conf. Ser. 1861(1), 012009 (2021). https://doi.org/10.1088/1742-6596/1861/1/012009

    Article  Google Scholar 

  45. Pseudorandom Number Sequence Test Program. https://www.fourmilab.ch/random/. Accessed 01 Feb 2023

  46. DIEHARD. https://tams.informatik.uni-hamburg.de/paper/2001/SA_Witt_Hartmann/cdrom/Internetseiten/stat.fsu.edu/diehard.html. Accessed 01 Feb 2023.

  47. L’Ecuyer, P., Simard, R.: TestU01: a C library for empirical testing of random number generators. ACM Trans. Math. Softw. 33(4), 1–40 (2007). https://doi.org/10.1145/1268776.1268777

    Article  MathSciNet  Google Scholar 

  48. Fernando, K.R., Tsokos, C.P.: Deep and statistical learning in biomedical imaging: State of the art in 3D MRI brain tumor segmentation. Inf. Fusion (2022)

  49. Gao, H., Miao, Q., Ma, D., Liu, R.: Deep mutual learning for brain tumor segmentation with the fusion network. Neurocomputing 521, 213–220 (2023)

    Article  Google Scholar 

  50. Kaur, R., Singh, S.: A comprehensive review of object detection with deep learning. Digit. Signal Process. 132, 103812 (2023)

    Article  Google Scholar 

  51. Tunali, V.: Improved prioritization of software development demands in Turkish with deep learning-based NLP. IEEE Access 10, 40249–40263 (2022). https://doi.org/10.1109/ACCESS.2022.3167269

    Article  Google Scholar 

  52. Patnaik, S.K., Babu, C.N., Bhave, M.: Intelligent and adaptive web data extraction system using convolutional and long short-term memory deep learning networks. Big Data Min. Anal. 4(4), 279–297 (2021). https://doi.org/10.26599/BDMA.2021.9020012

    Article  Google Scholar 

  53. Wang, S., Cao, J., Yu, P.S.: Deep learning for spatio-temporal data mining: a survey. IEEE Trans. Knowl. Data Eng. 34(8), 3681–3700 (2022). https://doi.org/10.1109/TKDE.2020.3025580

    Article  Google Scholar 

  54. Durga, B.K., Rajesh, V.: A ResNet deep learning based facial recognition design for future multimedia applications. Comput. Electr. Eng. 104, 108384 (2022)

    Article  Google Scholar 

  55. Ge, H., Zhu, Z., Dai, Y., Wang, B., Wu, X.: Facial expression recognition based on deep learning. Comput. Methods Programs Biomed. 215, 106621 (2022)

    Article  Google Scholar 

  56. Hadi, B., Khosravi, A., Sarhadi, P.: Deep reinforcement learning for adaptive path planning and control of an autonomous underwater vehicle. Appl. Ocean Res. 129, 103326 (2022)

    Article  Google Scholar 

  57. Wang, Z., Li, Y., Ma, C., Yan, X., Jiang, D.: Path-following optimal control of autonomous underwater vehicle based on deep reinforcement learning. Ocean Eng. 15(268), 113407 (2023)

    Article  Google Scholar 

  58. Mozaffari, S., Al-Jarrah, O.Y., Dianati, M., Jennings, P., Mouzakitis, A.: Deep learning-based vehicle behavior prediction for autonomous driving applications: a review. IEEE Trans. Intell. Transp. Syst. 23(1), 33–47 (2022). https://doi.org/10.1109/TITS.2020.3012034

    Article  Google Scholar 

  59. Dey, A.: Deep IDS : a deep learning approach for Intrusion detection based on IDS 2018. In: 2020 2nd international conference on sustainable technologies for industry 4.0 (STI), pp. 1–5 (2020). https://doi.org/10.1109/STI50764.2020.9350411

  60. Ince, K.: A novel approach for intrusion detection systems: V-IDS. Turk. J. Electr. Eng. Comput. Sci. 29(4), 1929–1943 (2021). https://doi.org/10.3906/elk-2005-1

    Article  Google Scholar 

  61. Alulema Flores, A.S.: Deep learning methods in natural language processing. In: Applied Technologies, Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds.), in Communications in Computer and Information Science, pp. 92–107. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-42520-3_8

  62. Suciu, A., Nagy, I., Marton, K., Pinca, I.: Parallel implementation of the NIST statistical test suite. In: Proceedings of the 2010 IEEE 6th international conference on intelligent computer communication and processing, pp. 363–368 (2010). https://doi.org/10.1109/ICCP.2010.5606412

  63. Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 374(2065), 20150202 (2016). https://doi.org/10.1098/rsta.2015.0202

    Article  MathSciNet  Google Scholar 

  64. Okagbue, H.I., Opanuga, A.A., Oguntunde, P.E., Ugwoke, P.O.: Random number datasets generated from statistical analysis of randomly sampled GSM recharge cards. Data brief. 10, 269–276 (2017)

    Article  Google Scholar 

  65. İnce, K.: Security analysis of java secure random library. Avrupa Bilim Ve Teknol. Derg. (2021). https://doi.org/10.31590/ejosat.900956

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the projects of the İnönü University Scientific Research Projects Department (SRPD) numbered FBG-2020-2143. The author would like to thank İnönü University SRPD for their valuable feedback.

Author information

Authors and Affiliations

  1. Software Engineering Department, İnönü University, 44200, Malatya, Turkey

    Kenan Ince

Authors
  1. Kenan Ince
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

There are only one author

Corresponding author

Correspondence to Kenan Ince.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

There is no situation that requires any ethical permission in the study.

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

Ince, K. Exploring the potential of deep learning and machine learning techniques for randomness analysis to enhance security on IoT. Int. J. Inf. Secur. 23, 1117–1130 (2024). https://doi.org/10.1007/s10207-023-00783-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10207-023-00783-y

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy