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A Proposed Intrusion Detection Method Based on Machine Learning Used for Internet of Things Systems

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 451)

Abstract

This paper presents an improved method using supervised machine-learning techniques of the Internet of things (IoT) systems to ensure security in deployments devices. The method increases accuracy and efficiency, identifies patterns, and makes decisions with significantly reduced error. In this work, we compare previous works by our improved ML method for both binary and multi-class classification on some IoT datasets. Based on metric parameters such as accuracy, precision, recall, F1 score, and ROC-AUC, the simulation results reveal that Classification and Regression Trees (CART) outperforms on all types of attacks in binary classification with an accuracy of 99% and with an accuracy between 21% and 37% higher than the original one. However, in multi-class classification, Naive Bayes (NB) outperforms other ML algorithms with an accuracy of 99% and an accuracy between 1% and 4% higher than the others works.

Keywords

  • Internet of things
  • Machine learning
  • Cybersecurity
  • Intrusion detection system
  • Artificial Intelligence

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Fig. 1.

References

  1. Gowda, V.D., et al.: Internet of Things: Internet revolution, impact, technology road map and features. Adv. Math. Sci. J. 9(7), 4405–4414 (2020)

    CrossRef  Google Scholar 

  2. Yousefnezhad, N., Avleen, M., Kary, F.: Security in the product lifecycle of IoT devices: a survey. J. Netw. Comput. Appl. 102779 (2020)

    Google Scholar 

  3. Rondon, L.P., et al.: Survey on enterprise Internet-of-Things systems (E-IoT): a security perspective. Ad Hoc Netw. 125, 102728 (2022)

    CrossRef  Google Scholar 

  4. Guo, G.: A Machine learning framework for intrusion detection system in IoT networks using an ensemble feature selection method. In: 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0593–05992021). https://doi.org/10.1109/IEMCON53756.2021.9623082

  5. Ahmad, Z., et al.: Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommun. Technol. 32(1), e4150 (2021)

    MathSciNet  Google Scholar 

  6. Kilincer, I.F., Ertam, F., Sengur, A.: Machine learning methods for cyber security intrusion detection: Datasets and comparative study. Comput. Netw. 188, 107840 (2021). https://doi.org/10.1016/j.comnet.2021.107840

    CrossRef  Google Scholar 

  7. Sarker, I.H.: CyberLearning: effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet of Things 14, 100393 (2021). https://doi.org/10.1016/j.iot.2021.100393

    CrossRef  Google Scholar 

  8. Ma, X., Cheng, X.: Detection and analysis of network intrusion data set based on KNN algorithm. World Sci. Res. J. 7(6), 118–123 (2021)

    Google Scholar 

  9. Kaushik, R., Singh, V., Kumar, R.: Multi-class SVM based network intrusion detection with attribute selection using infinite feature selection technique. J. Discr. Math. Sci. Cryptog. 24(8), 2137–2153 (2021)

    MATH  Google Scholar 

  10. Khan, M.A., et al.: Voting classifier-based intrusion detection for IoT networks. In: Saeed, F., Al-Hadhrami, T., Mohammed, E., Al-Sarem, M. (eds.) Advances on Smart and Soft Computing: Proceedings of ICACIn 2021, pp. 313–328. Springer Singapore, Singapore (2022). https://doi.org/10.1007/978-981-16-5559-3_26

    CrossRef  Google Scholar 

  11. Wester, P., Fredrik, H., Robert, L.: Anomaly-based intrusion detection using tree augmented naive bayes. In: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW). IEEE (2021)

    Google Scholar 

  12. Alshamy, R., et al.: Intrusion detection model for imbalanced dataset using SMOTE and random forest algorithm. In: International Conference on Advances in Cyber Security. Springer, Singapore (2021)

    Google Scholar 

  13. Noureen, S.S., et al.: Anomaly detection in the cyber-physical system using logistic regression analysis. In: 2019 IEEE Texas Power and Energy Conference (TPEC). IEEE (2019)

    Google Scholar 

  14. Shen, Z., Yuhao, Z., Weiying, C.: A bayesian classification intrusion detection method based on the fusion of PCA and LDA. Secur. Commun. Netw. 2019 (2019)

    Google Scholar 

  15. Rhohim, A., Vera, S., Muhammad Arief, N.: Denial of service traffic validation using K-fold cross-validation on software defined network. eProc. Eng. 8(5) (2021)

    Google Scholar 

  16. Moustafa, N.: New generations of Internet of Things datasets for cybersecurity applications based machine learning: TON_IoT datasets. In: Proceedings of the eResearch Australasia Conference, Brisbane, Australia (2019)

    Google Scholar 

  17. Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., Anwar, A.: TON_IoT telemetry dataset: a new generation dataset of IoT and IIoT for data-driven intrusion detection systems. IEEE Access 8, 165130–165150 (2020). https://doi.org/10.1109/ACCESS.2020.3022862

    CrossRef  Google Scholar 

  18. Pooja, T.S., Purohit, S.: Evaluating neural networks using Bi-Directional LSTM for network IDS (intrusion detection systems) in cyber security. Glob. Transit. Proc. 2(2), 448–454 (2021)

    CrossRef  Google Scholar 

  19. Ferrag, M.A., et al.: Deep learning-based intrusion detection for distributed denial of service attack in agriculture 4.0. Electronics 10(11), 1257 (2021)

    Google Scholar 

  20. Khan, A., Chase, C.: Detecting attacks on IoT devices using featureless 1D-CNN. In: 2021 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE (2021)

    Google Scholar 

  21. Park, S.H., Hyun, J.P., Young-June, C.: RNN-based prediction for network intrusion detection. In: 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE (2020)

    Google Scholar 

  22. Swarnalatha, G.: Detect and classify the unpredictable cyber-attacks by using DNN model. Turkish J. Comput. Math. Educ. (TURCOMAT) 12(6), 74–81 (2021)

    CrossRef  Google Scholar 

  23. Gulowaty, B., Ksieniewicz, P.: SMOTE algorithm variations in balancing data streams. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A.J., Menezes, R., Allmendinger, R. (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2019: 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part II, pp. 305–312. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-33617-2_31

    CrossRef  Google Scholar 

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Correspondence to Neder Karmous .

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Karmous, N., Aoueileyine, M.OE., Abdelkader, M., Youssef, N. (2022). A Proposed Intrusion Detection Method Based on Machine Learning Used for Internet of Things Systems. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_4

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