SoS-RPL: Securing Internet of Things Against Sinkhole Attack Using RPL Protocol-Based Node Rating and Ranking Mechanism

TL;DR

This paper proposes SoS-RPL, a novel mechanism for detecting sinkhole attacks in IoT networks by rating and ranking nodes using RPL protocol, improving security and network performance.

Contribution

It introduces a two-step sinkhole detection method in RPL-based IoT networks, combining node ranking and misbehavior source identification, which is a new approach.

Findings

Enhanced detection rate of sinkhole attacks

Reduced false-negative and false-positive rates

Improved packet delivery and throughput

Abstract

Through the Internet of Things (IoT) the internet scope is established by the integration of physical things to classify themselves into mutual things. A physical thing can be created by this inventive perception to signify itself in the digital world. Regarding the physical things that are related to the internet, it is worth noting that considering numerous theories and upcoming predictions, they mostly require protected structures, moreover, they are at risk of several attacks. IoTs are endowed with particular routing disobedience called sinkhole attack owing to their distributed features. In these attacks, a malicious node broadcasts illusive information regarding the routings to impose itself as a route towards specific nodes for the neighboring nodes and thus, attract data traffic. RPL (IP-V6 routing protocol for efficient and low-energy networks) is a standard routing protocol which is mainly employed in sensor networks and IoT. This protocol is called SoS-RPL consisting of two key sections of the sinkhole detection. In the first section rating and ranking the nodes in the RPL is carried out based on distance measurements. The second section is in charge of discovering the misbehavior sources within the IoT network through, the Average Packet Transmission RREQ (APT-RREQ). Here, the technique is assessed through wide simulations performed within the NS-3 environment. Based on the results of the simulation, it is indicated that the IoT network behavior metrics are enhanced based on the detection rate, false-negative rate, false-positive rate, packet delivery rate, maximum throughput, and packet loss rate.