LoRaLOFT-A Local Outlier Factor-based Malicious Nodes detection Method on MAC Layer for LoRaWAN

Document Type

Conference Proceeding

Source of Publication

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Publication Date

12-8-2022

Abstract

LoRaWAN is one of the network technologies that provide a long-range wireless network at low energy consumption. However, the pure Aloha MAC protocol and the duty-cycle limitation at both end devices and gateway make LoRaWAN very sensitive to malicious behaviors in the MAC layer. Moreover, this kind of sensitivity makes the false-positives problem challenging for malicious behavior detection with simple threshold methods. This study investigates two malicious behaviors - greedy and attack on the MAC layer. Furthermore, by combining the threshold method with a Local Outlier Factor (LOF) model in machine learning, LoRaLOFT is proposed. It is a centralized malicious node detection method. Analytical results show that the proposed method gives high detection accuracy while significantly reducing the false-positive rate in both behaviors.

ISBN

978-1-6654-3540-6

Publisher

IEEE

Volume

00

First Page

2026

Last Page

2031

Disciplines

Computer Sciences

Keywords

Energy consumption, Sensitivity, Wireless networks, Machine learning, Logic gates, Media Access Protocol, Feature extraction

Indexed in Scopus

no

Open Access

no

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