Abstract
Intrusions into the networks of the connected objects are rapidly evolving and affect its entire architecture (physical, network, application layers), as devices, networks and applications are increasingly connected and integrated. Securing these systems, which are generally constrained in resources, is becoming a necessity. Intrusion Detection Systems have proven to be an important security tool to detect attacks on the IoT network and resources. To create the IDS, Security researchers have recently used machine learning techniques because of the excellent results given by these methods (image and voice recognition, product recommendation, detection of spam and financial fraud …). Deep learning methods known for his or her successful ability to extract high-level functionality from big data are often a resilient mechanism for detecting small variants of attacks. The target of this work is to provide a general study on IDSs implementation techniques for IoT, precisely the classical methods and also the machine learning techniques. Finally, we give some recommendations of selected works that have practiced each of the methods presented.
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Bamou, A., EL Ouadghiri, M.D., Aghoutane, B. (2022). Current Works on IDS Development Strategies for IoT. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds) WITS 2020. Lecture Notes in Electrical Engineering, vol 745. Springer, Singapore. https://doi.org/10.1007/978-981-33-6893-4_2
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