{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/116152"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/116152","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Data-driven Estimation of Low-Power Long-Range Signal Parameters by an Unauthenticated Agent using Software Radio","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Keshabhoina, Tarun Rao"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Reed, Jeffrey H.","Muller Vasconcelos, Marcos"],"committee_members":["Pereira da Silva, Aloizio"],"year":2023,"date_issued":"2023-08-28","date_published":"2023-08-28","updated_at":"2026-07-22T22:20:38Z","subjects":["Neural Networks","Jamming","LoRa","Denial-of-Service","Software Radio"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:38400"],"render_values":[{"text":"vt_gsexam:38400","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/116152","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Reed, Jeffrey H.","Muller Vasconcelos, Marcos"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Pereira da Silva, Aloizio"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Keshabhoina, Tarun Rao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-08-29T08:00:56Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-08-29T08:00:56Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-08-28"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neural Networks","Jamming","LoRa","Denial-of-Service","Software Radio"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:38400"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/116152"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["When many independent devices (or agents) work together in a large system, they often need to communicate with each other. They do so using low-powered networks and often in an intermittent manner. These networks operate on unlicensed radio frequencies, which are open to interference, unwanted snooping, and 'denial-of-service' attacks that could shut down communication. In our study, we focus on a popular low-power, long-distance communication protocol called LoRa. Despite being designed to handle interference and noise well, related literature revealed that LoRa is vulnerable to a specific type of interference caused by 'chirp' signals. Current techniques to jam these signals and disrupt communication require the knowledge of two important factors - bandwidth and spreading factor. Normally, only authorized parties in the network would know these details, not any outsiders looking to interfere. However, we exploit LoRa's vulnerability without knowing these two parameters. By identifying a pattern in LoRa signals, we designed an artificial intelligence model that can determine these two parameters just by listening in. We then ran simulations and conducted experiments to understand how susceptible LoRa is to being detected under various levels of signal strength and noise. We also prepared a dataset of LoRa signals and used this data to confirm the effectiveness of our model."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Data-driven Estimation of Low-Power Long-Range Signal Parameters by an Unauthenticated Agent using Software Radio"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Reed, Jeffrey H.","Muller Vasconcelos, Marcos"],"dc:contributor.committeemember":["Pereira da Silva, Aloizio"],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Keshabhoina, Tarun Rao"],"dc:date.accessioned":["2023-08-29T08:00:56Z"],"dc:date.available":["2023-08-29T08:00:56Z"],"dc:date.issued":["2023-08-28"],"dc:description.abstract":["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."],"dc:description.abstractgeneral":["When many independent devices (or agents) work together in a large system, they often need to communicate with each other. They do so using low-powered networks and often in an intermittent manner. These networks operate on unlicensed radio frequencies, which are open to interference, unwanted snooping, and 'denial-of-service' attacks that could shut down communication. In our study, we focus on a popular low-power, long-distance communication protocol called LoRa. Despite being designed to handle interference and noise well, related literature revealed that LoRa is vulnerable to a specific type of interference caused by 'chirp' signals. Current techniques to jam these signals and disrupt communication require the knowledge of two important factors - bandwidth and spreading factor. Normally, only authorized parties in the network would know these details, not any outsiders looking to interfere. However, we exploit LoRa's vulnerability without knowing these two parameters. By identifying a pattern in LoRa signals, we designed an artificial intelligence model that can determine these two parameters just by listening in. We then ran simulations and conducted experiments to understand how susceptible LoRa is to being detected under various levels of signal strength and noise. We also prepared a dataset of LoRa signals and used this data to confirm the effectiveness of our model."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:38400"],"dc:identifier.uri":["http://hdl.handle.net/10919/116152"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Neural Networks","Jamming","LoRa","Denial-of-Service","Software Radio"],"dc:title":["Data-driven Estimation of Low-Power Long-Range Signal Parameters by an Unauthenticated Agent using Software Radio"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:38Z"}