SDR for Physical Layer Authentication
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/152400 |
Resumo: | Wireless networks and devices are easy and useful solutions nowadays, regardless of the context in which they are implemented. However, it is in the broadcast nature of wireless networks that some vulnerabilities arise. To protect against these vulnerabilities, encryp- tion and authentication methods are commonly used. However, such methods come at the expense of their own complexity, requiring high enough computational power to solve, and introducing latency. To try to reduce the complexity of the conventional ways of user authentication, this work has studied mechanisms to implement reliable authentication at the physical layer, analyzing the various devices signal characteristics. To achieve this analysis, the GNU Radio platform was used to process incoming signals and extract the necessary features. Given the open source nature of GNU Radio, this provides a customiz- able and low-cost solution to signal processing and feature extraction. This research uses the GNU Radio to implement a feature extraction solution and constructs a feature vector with size 1 × 95. This thesis studies the extracted features of eleven IEEE 802.15.4 devices in regards to their separability and proposes a solution for feature reduction. The feature vectors are passed through a Random Forest and a Deep Neural Network (DNN) classifier, achieving accuracies as high as 99% for short distance communication. |
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SDR for Physical Layer AuthenticationPHYAuthenticationFingerprinting802.15.4ZigbeeSDRDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaWireless networks and devices are easy and useful solutions nowadays, regardless of the context in which they are implemented. However, it is in the broadcast nature of wireless networks that some vulnerabilities arise. To protect against these vulnerabilities, encryp- tion and authentication methods are commonly used. However, such methods come at the expense of their own complexity, requiring high enough computational power to solve, and introducing latency. To try to reduce the complexity of the conventional ways of user authentication, this work has studied mechanisms to implement reliable authentication at the physical layer, analyzing the various devices signal characteristics. To achieve this analysis, the GNU Radio platform was used to process incoming signals and extract the necessary features. Given the open source nature of GNU Radio, this provides a customiz- able and low-cost solution to signal processing and feature extraction. This research uses the GNU Radio to implement a feature extraction solution and constructs a feature vector with size 1 × 95. This thesis studies the extracted features of eleven IEEE 802.15.4 devices in regards to their separability and proposes a solution for feature reduction. The feature vectors are passed through a Random Forest and a Deep Neural Network (DNN) classifier, achieving accuracies as high as 99% for short distance communication.Redes e dispositivos sem fio são implementações úteis e fáceis de realizar atualmente, independentemente do contexto em que são desenvolvidas. No entanto, é na natureza de difusão destas redes que surgem algumas vulnerabilidades. Métodos de criptografia e autenticação são usualmente utilizados para proteger contra essas vulnerabilidades. No entanto, esses métodos apresentam uma complexidade inerente, necessitando de poder computacional e introduzindo latência. Para tentar reduzir a complexidade das formas convencionais de autenticação de utilizadores das redes, esta dissertação estudou me- canismos para implementar uma autenticação fiável na camada física, analisando as ca- racterísticas dos sinais dos diversos dispositivos que utilizam a rede. Para realizar esta análise, a plataforma GNU Radio foi utilizada para processar sinais recebidos e extrair as características necessárias. Dada a natureza de código aberto do GNU Radio, é possível desenvolver uma solução customizável e de baixo custo. Esta dissertação utiliza o GNU Radio para implementar uma solução de extração de características e constrói um vetor de características de tamanho 1×95. Esta dissertação estuda as características extraídas de onze dispositivos IEEE 802.15.4 em relação à separabilidade destas e propõe uma solução para redução de características. Os vetores são passados por um classificador de Florestas Aleatórias (Random Forest) e um classificador de Redes Neurais Profundas, atingindo precisões de até 99% para comunicação a curta distância.Bernardo, LuísRUNFaria, João Francisco Lopes2023-05-04T11:44:31Z2022-112022-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/152400enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T18:11:10Zoai:run.unl.pt:10362/152400Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T18:11:10Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
SDR for Physical Layer Authentication |
title |
SDR for Physical Layer Authentication |
spellingShingle |
SDR for Physical Layer Authentication Faria, João Francisco Lopes PHY Authentication Fingerprinting 802.15.4 Zigbee SDR Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
title_short |
SDR for Physical Layer Authentication |
title_full |
SDR for Physical Layer Authentication |
title_fullStr |
SDR for Physical Layer Authentication |
title_full_unstemmed |
SDR for Physical Layer Authentication |
title_sort |
SDR for Physical Layer Authentication |
author |
Faria, João Francisco Lopes |
author_facet |
Faria, João Francisco Lopes |
author_role |
author |
dc.contributor.none.fl_str_mv |
Bernardo, Luís RUN |
dc.contributor.author.fl_str_mv |
Faria, João Francisco Lopes |
dc.subject.por.fl_str_mv |
PHY Authentication Fingerprinting 802.15.4 Zigbee SDR Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
topic |
PHY Authentication Fingerprinting 802.15.4 Zigbee SDR Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática |
description |
Wireless networks and devices are easy and useful solutions nowadays, regardless of the context in which they are implemented. However, it is in the broadcast nature of wireless networks that some vulnerabilities arise. To protect against these vulnerabilities, encryp- tion and authentication methods are commonly used. However, such methods come at the expense of their own complexity, requiring high enough computational power to solve, and introducing latency. To try to reduce the complexity of the conventional ways of user authentication, this work has studied mechanisms to implement reliable authentication at the physical layer, analyzing the various devices signal characteristics. To achieve this analysis, the GNU Radio platform was used to process incoming signals and extract the necessary features. Given the open source nature of GNU Radio, this provides a customiz- able and low-cost solution to signal processing and feature extraction. This research uses the GNU Radio to implement a feature extraction solution and constructs a feature vector with size 1 × 95. This thesis studies the extracted features of eleven IEEE 802.15.4 devices in regards to their separability and proposes a solution for feature reduction. The feature vectors are passed through a Random Forest and a Deep Neural Network (DNN) classifier, achieving accuracies as high as 99% for short distance communication. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-11 2022-11-01T00:00:00Z 2023-05-04T11:44:31Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/152400 |
url |
http://hdl.handle.net/10362/152400 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
_version_ |
1817545931532271616 |