Machine learning-based solutions for securing IoT systems against multilayer attacks

Book chapter

Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Machine learning-based solutions for securing IoT systems against multilayer attacks. in: Singh Tomar, R., Verma, S., Kumar Chaurasia, B., Singh, V., Abawajy, J. H., Akashe, S., Hsiung, Pao-Ann and Prasad, R. (ed.) Communication, Networks and Computing Third International Conference, CNC 2022, Gwalior, India, December 8–10, 2022, Proceedings, Part I Cham Springer. pp. 140-153
AuthorsAl Sukhni, B., Manna, S., Dave, J. and Zhang, L.
EditorsSingh Tomar, R., Verma, S., Kumar Chaurasia, B., Singh, V., Abawajy, J. H., Akashe, S., Hsiung, Pao-Ann and Prasad, R.
Abstract

IoT systems are prone to security attacks from several IoT layers as most of them possess limited resources and are unable to implement standard security protocols. This paper distinguishes multilayer IoT attacks from single-layer attacks and investigates their functioning. For developing a robust and efficient IDS (intrusion detection system), we have trained a few machine learning (ML) approaches such as NB, DT, and SVM using three standard sets of IoT datasets (Bot-IoT, ToN-IoT, Edge-IIoTset). Instead of using all features, the ML models are trained with similar features of multilayer IoT attacks to use optimal computational power and minimum number of features in the training dataset. The NB model achieves an accuracy of 57%–75%, while the DT model achieves an accuracy of 93%–100%. The outcome of the two ML models reveals that training with similar features possesses a higher accuracy level.

KeywordsIoT device; Multilayer attacks; Machine learning; Similar features
Page range140-153
Year2022
Book titleCommunication, Networks and Computing Third International Conference, CNC 2022, Gwalior, India, December 8–10, 2022, Proceedings, Part I
PublisherSpringer
Output statusPublished
Place of publicationCham
SeriesCommunications in Computer and Information Science
ISBN9783031431395
9783031431401
Publication dates
Online27 Sep 2023
Publication process dates
Deposited18 Oct 2023
Official URLhttps://link.springer.com/chapter/10.1007/978-3-031-43140-1_13
Related URLhttps://link.springer.com/book/10.1007/978-3-031-43140-1
References

1. Ferrag, M.A., Friha, O., Hamouda, D., Maglaras, L. and Janicke, H., 2022. Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. IEEE Access, 10, pp.40281-40306.
2. Anthi, E., Williams, L., Słowińska, M., Theodorakopoulos, G. and Burnap, P., 2019. A supervised intrusion detection system for smart home IoT devices. IEEE Internet of Things Journal, 6(5), pp.9042-9053.
3. Daws, R. (2021) Kaspersky: Attacks on IoT devices double in a year, Internet of Things News. IoT Tech News. Available at: https://www.iottechnews.com/news/2021/sep/07/kaspersky-attacks-on-io... (Accessed: October 31, 2022).
4. S. Khanam, I. B. Ahmedy, M. Y. Idna Idris, M. H. Jaward, and A. Q. Bin Md Sabri, “A survey of security challenges, attacks taxonomy and advanced countermeasures in the internet of things,” IEEE Access, vol. 8, pp. 219709–219743, 2020.
5. S. M. Tahsien, H. Karimipour, and P. Spachos, “Machine learning based solutions for security of Internet of Things (IoT): A survey,” J. Netw. Comput. Appl., vol. 161, no. 102630, p. 102630, 2020.
6. M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A survey of machine and deep learning methods for internet of things (IoT) security,” IEEE Commun. Surv. Tutor., vol. 22, no. 3, pp. 1646–1685, 2020.
7. P. Malhotra, Y. Singh, P. Anand, D. K. Bangotra, P. K. Singh, and W.-C. Hong, “Internet of Things: Evolution, concerns and security challenges,” Sensors (Basel), vol. 21, no. 5, p. 1809, 2021.
8. V. Hassija, V. Chamola, V. Saxena, D. Jain, P. Goyal, and B. Sikdar, “A survey on IoT security: Application areas, security threats, and solution architectures,” IEEE Access, vol. 7, pp. 82721–82743, 2019.
9. I. Butun, P. Osterberg, and H. Song, “Security of the internet of things: Vulnerabilities, attacks, and countermeasures,” IEEE Commun. Surv. Tutor., vol. 22, no. 1, pp. 616–644, 2020.
10. IBM (2022) IBM Security X-Force Threat Intelligence Index, Ibm.com. Available at: https://www.ibm.com/reports/threat-intelligence/ (Accessed: November 1, 2022).
11. Rehman, S. ur, Khaliq, M., Imtiaz, S. I., Rasool, A., Shafiq, M., Javed, A. R., Jalil, Z., & Bashir, A. K. (2021). DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU). Future Generations Computer Systems: FGCS, 118, 453–466. https://doi.org/10.1016/j.future.2021.01.022.
12. S. S. Priya, M. Sivaram, D. Yuvaraj, and A. Jayanthiladevi, “Machine Learning based DDOS Detection,” in 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), 2020.
13. R. Doshi, N. Apthorpe, and N. Feamster, “Machine learning DDoS detection for consumer Internet of Things devices,” arXiv [cs.CR], 2018.
14. Mukhtar, N. et al. (2020) “Improved hybrid approach for side-channel analysis using efficient convolutional neural network and dimensionality reduction,” IEEE access: practical innovations, open solutions, 8, pp. 184298–184311. doi: 10.1109/access.2020.3029206.
15. M. Zolanvari, M. A. Teixeira, L. Gupta, K. M. Khan, and R. Jain, “Machine learning-based network vulnerability analysis of industrial internet of things,” IEEE Internet Things J., vol. 6, no. 4, pp. 6822–6834, 2019.
16. E. Anthi, L. Williams, M. Slowinska, G. Theodorakopoulos, and P. Burnap, “A supervised intrusion detection system for smart home IoT devices,” IEEE Internet Things J., vol. 6, no. 5, pp. 9042–9053, 2019.
17. R. Ahmad and I. Alsmadi, “Machine learning approaches to IoT security: A systematic literature review,” Internet of Things, vol. 14, no. 100365, p. 100365, 2021.
18. H. F. Atlam and G. B. Wills, IoT Security, Privacy, Safety and Ethics. Cham: Springer International Publishing, 2020, pp. 123–149.
19. A. Mitrokotsa, M. Rieback, and A. Tanenbaum, “Classifying rfid attacks and defenses,” Information Systems Frontiers, vol. 12, pp. 491–505, 11 2010.
20. Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Trans. emerg. telecommun. technol., vol. 32, no. 1, 2021.
21. R. Kumar and R. Sharma, “Leveraging blockchain for ensuring trust in iot: A survey,” Journal of King Saud University - Computer and Information Sciences, 2021.
22. Ferrag, M. A. et al. (2020) “RDTIDS: Rules and decision tree-based intrusion detection system for Internet-of-Things networks,” Future internet, 12(3), p. 44.
23. Manesh, M.R. and Kaabouch, N., 2019. Cyber-attacks on unmanned aerial system networks: Detection, countermeasure, and future research directions. Computers & Security, 85, pp.386-401.
24. Nawir, M., Amir, A., Yaakob, N. and Lynn, O.B., 2016, August. Internet of Things (IoT): Taxonomy of security attacks. In 2016 3rd international conference on electronic design (ICED) (pp. 321-326). IEEE.
25. Alhowaide, A., Alsmadi, I. and Tang, J. (2021) “Ensemble Detection Model for IoT IDS,” Internet of Things (Netherlands), 16. doi:10.1016/j.iot.2021.100435.
26. A. R. Gad, A. A. Nashat, and T. M. Barkat, “Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset,” IEEE Access, vol. 9, pp. 142206–142217, 2021.

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