Data-driven Estimation of Low-Power Long-Range Signal Parameters by an Unauthenticated Agent using Software Radio

Many large-scale distributed Multi-Agent Systems (MAS) exchange information over low- power communication networks. In such scenarios, agents communicate intermittently with each other, often with limited power and over unlicensed spectrum bands that are susceptible to interference, eavesdropping, and Denial-of-Service (DoS) attacks. In this work, we consider a popular low-power, long-range communication protocol known as LoRa. Despite LoRa's high tolerance for noise and interference, it was found vulnerable to interference from particular chirp-type signals. State-of-the-art signal jamming techniques that exploit this property require the knowledge of two sensitive parameters - Bandwidth (BW) and Spreading Factor (SF). However, such information is available only to authenticated parties on the network and not to an eavesdropping adversary. We expose LoRa's vulnerability to DoS attacks by designing an intelligent jammer that surpasses the need for prior knowledge of these parameters. Exploiting a structural pattern in LoRa signals, we propose a Neural Network (NN) implementation for jointly inferring the two parameters by eavesdropping. Through simulation and experimentation, we analyze the detection vulnerability of LoRa for each combination of these parameters at various Signal to Noise Ratio (SNR) values. This work also presents a Radio Frequency (RF) dataset of LoRa signals, which is used to validate our inference model through experimentation.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Keshabhoina, Tarun Rao

Chairs dc:contributor.committeechair

• Reed, Jeffrey H.
• Muller Vasconcelos, Marcos

Committee member dc:contributor.committeemember

• Pereira da Silva, Aloizio

• Neural Networks
• Jamming
• LoRa
• Denial-of-Service
• Software Radio

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:38400

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/116152