Logo PTI Logo FedCSIS

Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Spotting Cyber Breaches in IoT Devices

,

DOI: http://dx.doi.org/10.15439/2023F7136

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 1307–1310 ()

Full text

Abstract. In the ever-growing realm of the Internet of Things (IoT), ensuring the security of interconnected devices is of paramount importance. This paper discusses the process of spotting cyber breaches in IoT devices, a significant concern that needs urgent attention due to the susceptibility of these devices to hacking and other cyber threats. With billions of IoT devices worldwide, the detection and prevention of cybersecurity breaches are critical for maintaining the integrity and functionality of networks and systems. In this paper, we showcase the outcomes achieved by employing the LightGBM technique for a cyberattack prediction challenge, which was a part of the FedCSIS 2023 conference.

References

  1. T. Hastie, R. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition, ser. Springer Series in Statistics. Springer, 2009.
  2. G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “LightGBM: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., 2017, pp. 3146–3154.
  3. N. Koroniotis, N. Moustafa, E. Sitnikova, and J. Slay, “Towards developing network forensic mechanism for botnet activities in the IoT based on machine learning techniques,” in Mobile Networks and Management, J. Hu, I. Khalil, Z. Tari, and S. Wen, Eds. Cham: Springer International Publishing, 2018, pp. 30–44.
  4. Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai, “Kitsune: An ensemble of autoencoders for online network intrusion detection,” in Network and Distributed Systems Security (NDSS) Symposium, 2018.
  5. E. Hodo, X. Bellekens, A. Hamilton, P.-L. Dubouilh, E. Iorkyase, C. Tachtatzis, and R. Atkinson, “Threat analysis of IoT networks using artificial neural network intrusion detection system,” in 2016 International Symposium on Networks, Computers and Communications (ISNCC), 2016, pp. 1–6.
  6. Y. Meidan, M. Bohadana, A. Shabtai, M. Ochoa, N. O. Tippenhauer, J. D. Guarnizo, and Y. Elovici, “Detection of unauthorized IoT devices using machine learning techniques,” 2017.
  7. M. Aljabri, A. A. Alahmadi, R. M. A. Mohammad, F. Alhaidari, M. Aboulnour, D. M. Alomari, and S. Mirza, “Machine learning-based detection for unauthorized access to IoT devices,” Journal of Sensor and Actuator Networks, vol. 12, no. 2, 2023.
  8. C. Malathi and I. N. Padmaja, “Identification of cyber attacks using machine learning in smart iot networks,” Materials Today: Proceedings, vol. 80, pp. 2518–2523, 2023.
  9. M. Al-kasassbeh, M. A. Abbadi, and A. M. Al-Bustanji, “LightGBM algorithm for malware detection,” in Intelligent Computing, K. Arai, S. Kapoor, and R. Bhatia, Eds. Cham: Springer International Publishing, 2020, pp. 391–403.
  10. A. Anghel, N. Papandreou, T. Parnell, A. D. Palma, and H. Pozidis, “Benchmarking and optimization of gradient boosting decision tree algorithms,” 2019.
  11. C. Bentéjac, A. Csörgo, and G. Martínez-Muñoz, “A comparative analysis of gradient boosting algorithms,” Artificial Intelligence Review, vol. 54, pp. 1937–1967, 2020.
  12. A. Janusz, D. Kauza, A. Chdzyska-Krasowska, B. Konarski, J. Holland, and D. lzak, “Ieee bigdata 2019 cup: Suspicious network event recognition,” in 2019 IEEE International Conference on Big Data (Big Data), 2019. http://dx.doi.org/10.1109/BigData47090.2019.9005668 pp. 5881–5887.
  13. Q. H. Vu, D. Ruta, and L. Cen, “Gradient boosting decision trees for cyber security threats detection based on network events logs,” in 2019 IEEE International Conference on Big Data (Big Data), 2019. http://dx.doi.org/10.1109/BigData47090.2019.9006061 pp. 5921–5928.
  14. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’16. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939785 pp. 785–794.
  15. A. Janusz, M. Przyborowski, P. Biczyk, and D. lzak, “Network device workload prediction: A data mining challenge at knowledge pit,” in 2020 15th Conference on Computer Science and Information Systems (FedCSIS), 2020. http://dx.doi.org/10.15439/2020F159 pp. 77–80.
  16. S. Pioroński and T. Górecki, “Using gradient boosting trees to predict the costs of forwarding contracts,” in 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS), 2022. http://dx.doi.org/10.15439/2022F299 pp. 421–424.
  17. K. A. Spackman, “Signal detection theory: Valuable tools for evaluating inductive learning,” in Proceedings of the Sixth International Workshop on Machine Learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1989, pp. 160–163.
  18. R. L. Burden, Numerical analysis, 8th ed. Thomson Brooks/Cole, 2005.
  19. J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (ROC) curve.” Radiology, vol. 143, no. 1, pp. 29–36, 1982.
  20. “FedCSIS 2023 challenge: Cybersecurity threat detection in the behavior of IoT devices,” https://knowledgepit.ml/fedcsis-2023-challenge/, accessed: 2023-07-05.
  21. C. Wang, Q. Wu, M. Weimer, and E. Zhu, “FLAML: A fast and lightweight AutoML library,” in MLSys, 2021.