Greedy Behavior Detection With Machine Learning for LoRaWAN Network

Document Type

Article

Source of Publication

IEEE Transactions on Network and Service Management

Publication Date

1-1-2024

Abstract

—LoRaWAN (Long Range Wide Area Network) has garnered significant attention within the Internet of Things (IoT) due to its ability to establish a wireless network with massive devices over long distances while minimizing energy consumption. Our previous work shows its suitability for Intelligent Transportation Systems (ITS) scenarios. However, the utilization of the Aloha MAC protocol presents a challenge for LoRaWAN as it grapples with the presence of compromised nodes. These nodes may engage in greedy behaviors, disregarding network regulations to enhance their own performance or acquire additional network resources, and are often difficult to detect. This research contributes to machine learning-based greedy behavior detection methods. After proposing several end-to-end (E2E) methods with different ML algorithms, EDLoG (Encoder-based detection method of LoRaWAN Greedy behaviors) is proposed. It is a greedy behavior detection method combining a Multilayer Perceptron (MLP) encoder network and a statistical abnormal detection algorithm. The performance evaluations are conducted using simulation data under different scenarios given by MELoNS, a Modular and Extendable Simulator for the LoRaWAN Network developed in our previous work. The results show that the proposed method gives a detection recall 15%-20% higher than the baseline method by keeping a high detection precision. Moreover, the proposed methods show high timing efficiency with a running time much smaller than LoRaWAN’s time scale, making the method easily deployed to a real LoRaWAN network.

ISSN

1932-4537

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

21

Issue

3

First Page

2731

Last Page

2740

Disciplines

Computer Sciences

Keywords

greedy behavior, Internet of Things (IoT), LoRaWAN, machine learning, malicious detection, security

Scopus ID

85182372387

Indexed in Scopus

yes

Open Access

no

Link to Full Text

Share

COinS