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Detection and prevention of DDoS attacks on edge computing of IoT devices through reinforcement learning

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A Correction to this article was published on 28 December 2023

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Abstract

The limited resource and computation capacity of an IoT device always being a target vector for intruders to use to steal sensitive data from the device. Computational problems were later solved by the engineers by introducing an edge server near the IoT device network boundary. But still, it is not secure from the hands of intruders. IoT device vulnerabilities still can allow the Edge server to give the control of the device to the intruders. Hence both the Edge server and the connected IoT device may fall under the control of an intruder. Many security algorithms have been proposed after visualizing these security gaps, but none of them still promises full security to the IoT devices. In this paper, we will show how a very efficient machine learning method which is known as reinforcement learning can provide a better security solution on IoT Edge Computing, which is the most proficient and cost-efficient. We proposed the novel reinforcement learning approach which we termed as “Message Driven based Reinforcement Learning security of IoT-Edge computing (MD-RL)” and used the NS-2.35 simulator to experimentally validate this approach. In our experimental setup, we created a simulated wireless sensor network where different types of virtual nodes are set up. We intended to create the nodes which include the IoT node (s), where fewer and limited resources are assigned, whereas the node which is assigned with rich resources capabilities is taken as an Edge node. Various types of malware attacks malfunction the IoT network. But out of those Distributed Denial-of-Service (DDoS) attacks is found steadily increasing in the last few years and increased by almost one-third each year. Hence, we again simulated DDoS attacks over an IoT Edge computing network and generated a real-time-based scenario of data communication between an IoT device and the linked Edge server to validate and compare our proposed security solution for such a victim IoT network.

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  1. Lovely Professional University, Phagwara, 144411, India

    Anit Kumar & Dhanpratap Singh

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  1. Anit Kumar
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Kumar, A., Singh, D. Detection and prevention of DDoS attacks on edge computing of IoT devices through reinforcement learning. Int. j. inf. tecnol. 16, 1365–1376 (2024). https://doi.org/10.1007/s41870-023-01508-z

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