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

Conference paper

Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Machine learning-based solutions for securing IoT systems against multilayer attacks.
AuthorsAl Sukhni, B., Manna, S., Dave, J. and Zhang, L.
TypeConference paper
Description

With the advancement of technology, IoT systems have been widely used in all sectors from smart homes to healthcare, however, those devices are vulnerable to multilayer security attacks because most of the devices are resource-constrained and hence cannot implement standard security frameworks. In this paper, we have categorised the multilayer IoT attacks and analysed their behaviours. For developing a smart intrusion detection system, three machine learning models (NB, DT and SVM) are trained with three standard IoT datasets (Bot-IoT, ToN-IoT, Edge-IIoTset). To optimize the computational power and the number of features in the training dataset, similar features of multilayer IoT attacks are used for training ML models instead of all features. The achieved accuracy for the NB model is 57%-75% whereas for DT is 93%-100%. A comparison between the two approaches (training with all features and similar features) shows that the accuracy level is better in case of similar features.

KeywordsIoT device; Multilayer attacks; Machine learning; Similar features
Year2022
Conference3rd International Conference on Communication, Networks and Computing
Official URLhttp://event.itmuniversity.ac.in/cnc-2022/
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Web address (URL) of conference proceedingshttps://www.springer.com/series/7899
Publisher's version
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References

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Deposited05 Dec 2022
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