Skip to main content
SpringerLink
Log in

Vulnerability Analysis of IoT Devices to Cyberattacks Based on Naïve Bayes Classifier

  • Conference paper
  • First Online:
Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13758))

Included in the following conference series:

  • 652 Accesses

Abstract

IoT or Smart Word, as a global technology, is a rapidly growing concept of ICT systems interoperability covering many areas of life. Increasing the speed of data transmission, increasing the number of devices per square meter, reducing delays - all this is guaranteed by modern technologies in combination with the 5G standard. However, the key role is played by the aspect of protection and security of network infrastructure and the network itself. No matter what functions are to be performed by IoT, all devices included in such a system are connected by networks. IoT does not create a uniform environment, hence its vulnerability in the context of cybersecurity. This paper deals with the selection of a method to classify software vulnerabilities to cyber-attacks and threats in the network. The classifier will be created based on the Naive Bayes method. However, the quality analysis of the classifier, i.e., checking whether it classifies vulnerabilities correctly, was performed by plotting the ROC curve and analyzing the Area Under the Curve (AUC).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mahmoud, H., Thabet, M., Khafagy, M.H., Omara, F.A.: Multiobjective task scheduling in cloud environment using decision tree algorithm. IEEE Access 10, 36140–36151 (2022)

    Article  Google Scholar 

  2. Alex, S., Dhanaraj, K.J., Deepthi, P.P.: Private and energy-efficient decision tree-based disease detection for resource-constrained medical users in mobile healthcare network. IEEE Access 10, 17098–17112 (2022)

    Article  Google Scholar 

  3. Menezes, A.G., Araujo, M.M., Almeida, O.M., Barbosa, F.R., Braga, A.P.: Induction of decision trees to diagnose incipient faults in power transformers. IEEE Trans. Dielectr. Electr. Insul. 29(1), 279–286 (2022)

    Article  Google Scholar 

  4. Dash, S., Abraham, A., Luhach, A.K., Mizera-Pietraszko, J., Rodrigues, J.J.: Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis, Int. J. Distrib. Sens. Netw. 16(1), 1550147719895210 (2020)

    Google Scholar 

  5. Balagani, K.S., Phoha, V.V.: On the relationship between dependence tree classification error and Bayes error rate. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1866–1868 (2007)

    Article  Google Scholar 

  6. Dalton, L.A., Benalcázar, M.E., Brun, M., Dougherty, E.R.: Analytic representation of Bayes labeling and Bayes clustering operators for random labeled point processes. IEEE Trans. Signal Process. 63(6), 1605–1620 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  7. Mezghani, N., Mitiche, A., Cheriet, M.: Bayes classification of online Arabic characters by Gibbs modeling of class conditional densities. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1121–1131 (2008)

    Article  Google Scholar 

  8. Fang, X.: Inference-based Naïve Bayes: turning Naïve Bayes cost-sensitive. IEEE Trans. Knowl. Data Eng. 25(10), 2302–2313 (2013)

    Article  Google Scholar 

  9. Xue, Q., Zhu, Y., Wang, J.: Joint distribution estimation and naïve Bayes classification under local differential privacy. IEEE Trans. Emerg. Topics Comput. 9(4), 2053–2063 (2021)

    Article  Google Scholar 

  10. Xin, R., Zhang, J., Shao, Y.: Complex network classification with convolutional neural network. Tsinghua Sci. Technol. 25(4), 447–457 (2020)

    Article  Google Scholar 

  11. Huang, J., Huang, S., Zeng, Y., Chen, H., Chang, S., Zhang, Y.: Hierarchical digital modulation classification using cascaded convolutional neural network. J. Commun. Inf. Netw. 6(1), 72–81 (2021)

    Article  Google Scholar 

  12. Oong, T.H., Isa, N.A.M.: Adaptive evolutionary artificial neural networks for pattern classification. IEEE Trans. Neural Netw. 22(11), 1823–1836 (2011)

    Article  Google Scholar 

  13. Huk, M., Mizera-Pietraszko, J.: Contextual neural-network based spectrum prediction for cognitive radio. In: 4th International Conference on Future Generation Communication Technology (FGCT 2015), pp. 1–5. IEEE Computer Society, London (2015)

    Google Scholar 

  14. Huk, M., Mizera-Pietraszko, J.: Context-related data processing with artificial neural networks for higher reliability of telerehabilitation systems. In: 17th International Conference on E-health Networking, Application & Services (HealthCom), pp. 217–221. IEEE Computer Society, Boston (2015)

    Google Scholar 

  15. Bennett, J.C., et al.: Feature-based statistical analysis of combustion simulation data. IEEE Trans Visual. Comput. Graph. 17(12), 1822–1831 (2011)

    Article  Google Scholar 

  16. Wu, Y., Krishnan, S.: Statistical analysis of gait rhythm in patients with Parkinson’s disease. IEEE Trans. Neural Syst. Rehabil. Eng. 18(2), 150–158 (2010)

    Article  Google Scholar 

  17. Huk, M., Kwiatkowski, J., Konieczny, D., Kędziora, M., Mizera-Pietraszko, J.: Context-sensitive text mining with fitness leveling genetic algorithm. In: Proceedings of the IEEE CYBCONF 2015. New York, pp 183–188 (2015)

    Google Scholar 

  18. Paul, T.K., Iba, H.: Prediction of cancer class with majority voting genetic programming classifier using gene expression data. IEEE/ACM Trans. Comput. Biol. Bioinform. 6(2), 353–367 (2009)

    Article  Google Scholar 

  19. Huang, F., Li, X., Zhang, S., Zhang, J.: Harmonious genetic clustering. IEEE Trans. Cybern. 48(1), 199–214 (2018)

    Article  Google Scholar 

  20. Mizera-Pietraszko, J., Tancula, J., Jozwiak, I.: Rough set theory for optimization of packet management mechanism in IP routers. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds.) WAINA 2020. AISC, vol. 1150, pp. 1308–1320. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44038-1_119

    Chapter  Google Scholar 

  21. Maji, P., Garai, P.: IT2 fuzzy-rough sets and max relevance-max significance criterion for attribute selection. IEEE Trans. Cybern. 45(8), 1657–1668 (2015)

    Article  Google Scholar 

  22. Nowicki, A.: On combining neuro-fuzzy architectures with the rough set theory to solve classification problems with incomplete data. IEEE Trans. Knowl. Data Eng. 20(9), 1239–1253 (2008)

    Article  Google Scholar 

  23. Yao, J., Azam, N.: Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets. IEEE Trans. Fuzzy Syst. 23(1), 3–15 (2015)

    Article  Google Scholar 

  24. Karaolis, M.A., Moutiris, J.A., Hadjipanayi, D., Pattichis, C.S.: Assessment of the risk factors of coronary heart events based on data mining with decision trees. IEEE Trans. Inf. Technol. Biomed. 14(3), 559–566 (2010)

    Article  Google Scholar 

  25. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the Gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014)

    Article  MATH  Google Scholar 

  26. Es-Sabery, F., et al.: A MapReduce opinion mining for COVID-19-related tweets classification using enhanced ID3 decision tree classifier. IEEE Access 9, 58706–58739 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Military University of Land Forces, Wrocław, Poland

    Jolanta Mizera-Pietraszko

  2. Opole University, Opole, Poland

    Jolanta Tańcula

Authors
  1. Jolanta Mizera-Pietraszko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jolanta Tańcula
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jolanta Mizera-Pietraszko .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mizera-Pietraszko, J., Tańcula, J. (2022). Vulnerability Analysis of IoT Devices to Cyberattacks Based on Naïve Bayes Classifier. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21967-2_51

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21966-5

  • Online ISBN: 978-3-031-21967-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation