Enhancement Trust Management in IoT to Detect ON-OFF Attacks with Cooja

Authors : Ali Hamid FAREA, Kerem KÜÇÜK

Pages : 123-128

View : 35 | Download : 11

Publication Date : 2021-11-30

Article Type : Research Paper

Abstract :In IoT ecosystems, the interaction of devices with each other creates a perfect environment but there are heterogeneous nodes that will supply a variety of services. In the intelligent environment, devices with various processing capacities may operate together and communicate transparently with one other and with users. These IoT gadgets are frequently exposed to the public and interact over wireless channels, making them vulnerable to malicious attacks. ON-OFF attacks insert ignore into journalissuearticles values(OOAs); are regarded as one of the IoT`s trust threats. In these attacks, the malicious nodes alternate between behaving well and behaving badly, jeopardizing the network if they stay trusted nodes. In this paper, we introduce a model to enhance trust management in IoT to detect insert ignore into journalissuearticles values(OOAs); with the help of Artificial Neural Networks insert ignore into journalissuearticles values(ANN); to analyze the statuses insert ignore into journalissuearticles values(ON-OFF); and radio messages for each node which in turn assesses the resource trust automatically in IoT. We implemented our experiment by using Contiki Operating System insert ignore into journalissuearticles values(OS); and analyzed the data with Microsoft machine learning studio insert ignore into journalissuearticles values(MMLS); to display the results.
Keywords : Trust management, IoT, ON OFF attacks, Artificial Neural Networks, Security, Contiki OS

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