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

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