RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning

Semih Cakir,Nesibe Yalcin,Sinan Toklu

doi:10.1109/access.2020.3029191

Semih Cakir, Nesibe Yalcin + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3029191

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Abstract

Cyberattacks targeting Internet of Things (IoT), have increased significantly, over the past decade, with the spread of internet-connected smart devices and applications. Routing Protocol for Low-Power and Lossy Network (RPL) enables messages to be routed between nodes for the Wireless Sensor Network in the network layer. RPL protocol, which is sensitive and difficult to protect, is exposed to various attacks. These attacks negatively affect data transmission and cause great destruction to the topology by consuming the resources. Hello Flooding (HF) attacks against RPL cause consumption of constrained resources (memory, processing and energy) in nodes. Therefore, in this study, a Gated Recurrent Unit network model based deep learning has been proposed to predict and prevent HF attacks on RPL protocol in IoT networks. The proposed model has been compared with Support Vector Machine and Logistic Regression methods, and different power states and total energy consumptions of the nodes have been taken into consideration and experimented with. The results confirm the promised and expected performance from the model in terms of source efficiency and IoT security. In addition, attack detection has been carried out with a much lower error rate than literature studies for HF attacks from RPL flood attacks.

Highlights

Internet of Things (IoT) refers to devices, machines and software that communicate with each other when considered in a system, on the other hand everything connected to the internet [1]
The purpose of source-side attacks is that the malicious node, which is harmful in our highly sensitive network structure, creates energy consumption, process crowd, and excessive memory density to disturb the stability of the Quality of Service (QoS) of the network
GRU neural networks-based deep learning are preferred due to their simple structure, ability to learn the model faster with less data compared to LSTM

Summary

Introduction

Internet of Things (IoT) refers to devices, machines and software that communicate with each other when considered in a system, on the other hand everything connected to the internet [1]. The primary function of attacks on Routing Protocol for Low-Power and Lossy Network (RPL) based IoT networks, the most common type encountered in the literature, is to route messages between nodes. The purpose of source-side attacks is that the malicious node, which is harmful in our highly sensitive network structure, creates energy consumption, process crowd, and excessive memory density to disturb the stability of the Quality of Service (QoS) of the network. One of the most known attack types, aim to render the nodes dysfunctional by damaging the network topology with the help of the malicious node. They perform this by transmitting or broadcasting DODAG Information Solicitation (DIS) messages

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