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WITS 2020 pp 15-24 | Cite as

Current Works on IDS Development Strategies for IoT

Conference paper
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Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 745)

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.

Keywords

IoT security Intrusion-detection system (IDS) IDS implementation Learning method-based IDS 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2022

Authors and Affiliations

  1. 1.IA LaboratoryScience Faculty My Ismail University of MeknesMeknesMorocco

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