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

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 (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 (OOAs) with the help of Artificial Neural Networks (ANN) to analyze the statuses (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 (OS) and analyzed the data with Microsoft machine learning studio (MMLS) to display the results.

Keywords:

Trust management, IoT, ON-OFF attacks, Artificial Neural Networks Security, Contiki OS,

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