Mitigating Black Hole Attacks in Routing Protocols Using a Machine Learning-Based Trust Model

Abstract

Many application domains gain considerable advantages with the internet of things (IoT) network. It improves our lifestyle towards smartness in smart devices. IoT devices are mostly resource-constrained such as memory, battery, etc. So it is highly vulnerable to security attacks. Traditional security mechanisms can't be applied to these devices due to their restricted resources. A trust-based security mechanism plays an important role to ensure security in the IoT environment because it consumes only fewer resources. Thus, it is essential to evaluate the trustworthiness among IoT devices. The proposed model improves trusted routing in the IoT environment by detecting and isolating malicious nodes. This model uses reinforcement learning (RL) where the agent learns the behavior of the node and isolates the malicious nodes to improve the network performance. The model focuses on IoT with the routing protocol for low power and lossy network (RPL) and counters the blackhole attack.

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