Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/23454 |
Resumo: | Every novel technology adds hidden vulnerabilities ready to be exploited by a growing number of cyber-attacks. Automated software testing can be a promising solution to quickly analyze thousands of lines of code by generating and slightly modifying function-specific testing data to encounter a multitude of vulnerabilities and attack vectors. This process draws similarities to the constrained adversarial examples generated by adversarial learning methods, so there could be significant benefits to the integration of these methods in automated testing tools. Therefore, this systematic review is focused on the current state-of-the-art of constrained data generation methods applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance testing tools with adversarial learning methods and improve the resilience and robustness of their digital systems. The found constrained data generation applications for adversarial machine learning were systematized, and the advantages and limitations of approaches specific for software testing were thoroughly analyzed, identifying research gaps and opportunities to improve testing tools with adversarial attack methods. |
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Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic reviewSoftware developmentConstrained data generationAdversarial attacksMachine learningEvery novel technology adds hidden vulnerabilities ready to be exploited by a growing number of cyber-attacks. Automated software testing can be a promising solution to quickly analyze thousands of lines of code by generating and slightly modifying function-specific testing data to encounter a multitude of vulnerabilities and attack vectors. This process draws similarities to the constrained adversarial examples generated by adversarial learning methods, so there could be significant benefits to the integration of these methods in automated testing tools. Therefore, this systematic review is focused on the current state-of-the-art of constrained data generation methods applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance testing tools with adversarial learning methods and improve the resilience and robustness of their digital systems. The found constrained data generation applications for adversarial machine learning were systematized, and the advantages and limitations of approaches specific for software testing were thoroughly analyzed, identifying research gaps and opportunities to improve testing tools with adversarial attack methods.The present work was partially supported by the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within project "Cybers SeC IP" (NORTE-01-0145-FEDER- 000044). This work has also received funding from UIDB/00760/2020.Repositório Científico do Instituto Politécnico do PortoVitorino, JoãoDias, TiagoFonseca, TiagoMaia, EvaPraça, Isabel2023-09-05T14:12:55Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/23454eng10.48550/arXiv.2303.07546info: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:RCAAP2023-09-13T01:46:16Zoai:recipp.ipp.pt:10400.22/23454Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:28:18.645605Repositório 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 |
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review |
title |
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review |
spellingShingle |
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review Vitorino, João Software development Constrained data generation Adversarial attacks Machine learning |
title_short |
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review |
title_full |
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review |
title_fullStr |
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review |
title_full_unstemmed |
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review |
title_sort |
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review |
author |
Vitorino, João |
author_facet |
Vitorino, João Dias, Tiago Fonseca, Tiago Maia, Eva Praça, Isabel |
author_role |
author |
author2 |
Dias, Tiago Fonseca, Tiago Maia, Eva Praça, Isabel |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Vitorino, João Dias, Tiago Fonseca, Tiago Maia, Eva Praça, Isabel |
dc.subject.por.fl_str_mv |
Software development Constrained data generation Adversarial attacks Machine learning |
topic |
Software development Constrained data generation Adversarial attacks Machine learning |
description |
Every novel technology adds hidden vulnerabilities ready to be exploited by a growing number of cyber-attacks. Automated software testing can be a promising solution to quickly analyze thousands of lines of code by generating and slightly modifying function-specific testing data to encounter a multitude of vulnerabilities and attack vectors. This process draws similarities to the constrained adversarial examples generated by adversarial learning methods, so there could be significant benefits to the integration of these methods in automated testing tools. Therefore, this systematic review is focused on the current state-of-the-art of constrained data generation methods applied for adversarial learning and software testing, aiming to guide researchers and developers to enhance testing tools with adversarial learning methods and improve the resilience and robustness of their digital systems. The found constrained data generation applications for adversarial machine learning were systematized, and the advantages and limitations of approaches specific for software testing were thoroughly analyzed, identifying research gaps and opportunities to improve testing tools with adversarial attack methods. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-09-05T14:12:55Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/23454 |
url |
http://hdl.handle.net/10400.22/23454 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.48550/arXiv.2303.07546 |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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 |
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