Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach
Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Ed...
Ausführliche Beschreibung
Autor*in: |
Mishra, Kamta Nath [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Übergeordnetes Werk: |
Enthalten in: Cluster computing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998, 27(2022), 1 vom: 30. Nov., Seite 27-52 |
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Übergeordnetes Werk: |
volume:27 ; year:2022 ; number:1 ; day:30 ; month:11 ; pages:27-52 |
Links: |
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DOI / URN: |
10.1007/s10586-022-03813-x |
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Katalog-ID: |
SPR054883946 |
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520 | |a Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. | ||
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650 | 4 | |a Edge computing |7 (dpeaa)DE-He213 | |
650 | 4 | |a Federated learning systems |7 (dpeaa)DE-He213 | |
650 | 4 | |a Machine learning techniques |7 (dpeaa)DE-He213 | |
650 | 4 | |a Voting classifier |7 (dpeaa)DE-He213 | |
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700 | 1 | |a Saket, Shashwat |4 aut | |
700 | 1 | |a Mishra, Shivam Prakash |4 aut | |
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10.1007/s10586-022-03813-x doi (DE-627)SPR054883946 (SPR)s10586-022-03813-x-e DE-627 ger DE-627 rakwb eng Mishra, Kamta Nath verfasserin aut Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. Blockchain technology (dpeaa)DE-He213 Edge computing (dpeaa)DE-He213 Federated learning systems (dpeaa)DE-He213 Machine learning techniques (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Bhattacharjee, Vandana aut Saket, Shashwat aut Mishra, Shivam Prakash aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 27(2022), 1 vom: 30. Nov., Seite 27-52 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2022 number:1 day:30 month:11 pages:27-52 https://dx.doi.org/10.1007/s10586-022-03813-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 1 30 11 27-52 |
spelling |
10.1007/s10586-022-03813-x doi (DE-627)SPR054883946 (SPR)s10586-022-03813-x-e DE-627 ger DE-627 rakwb eng Mishra, Kamta Nath verfasserin aut Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. Blockchain technology (dpeaa)DE-He213 Edge computing (dpeaa)DE-He213 Federated learning systems (dpeaa)DE-He213 Machine learning techniques (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Bhattacharjee, Vandana aut Saket, Shashwat aut Mishra, Shivam Prakash aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 27(2022), 1 vom: 30. Nov., Seite 27-52 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2022 number:1 day:30 month:11 pages:27-52 https://dx.doi.org/10.1007/s10586-022-03813-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 1 30 11 27-52 |
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10.1007/s10586-022-03813-x doi (DE-627)SPR054883946 (SPR)s10586-022-03813-x-e DE-627 ger DE-627 rakwb eng Mishra, Kamta Nath verfasserin aut Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. Blockchain technology (dpeaa)DE-He213 Edge computing (dpeaa)DE-He213 Federated learning systems (dpeaa)DE-He213 Machine learning techniques (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Bhattacharjee, Vandana aut Saket, Shashwat aut Mishra, Shivam Prakash aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 27(2022), 1 vom: 30. Nov., Seite 27-52 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2022 number:1 day:30 month:11 pages:27-52 https://dx.doi.org/10.1007/s10586-022-03813-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 1 30 11 27-52 |
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10.1007/s10586-022-03813-x doi (DE-627)SPR054883946 (SPR)s10586-022-03813-x-e DE-627 ger DE-627 rakwb eng Mishra, Kamta Nath verfasserin aut Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. Blockchain technology (dpeaa)DE-He213 Edge computing (dpeaa)DE-He213 Federated learning systems (dpeaa)DE-He213 Machine learning techniques (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Bhattacharjee, Vandana aut Saket, Shashwat aut Mishra, Shivam Prakash aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 27(2022), 1 vom: 30. Nov., Seite 27-52 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2022 number:1 day:30 month:11 pages:27-52 https://dx.doi.org/10.1007/s10586-022-03813-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 1 30 11 27-52 |
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10.1007/s10586-022-03813-x doi (DE-627)SPR054883946 (SPR)s10586-022-03813-x-e DE-627 ger DE-627 rakwb eng Mishra, Kamta Nath verfasserin aut Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. Blockchain technology (dpeaa)DE-He213 Edge computing (dpeaa)DE-He213 Federated learning systems (dpeaa)DE-He213 Machine learning techniques (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Bhattacharjee, Vandana aut Saket, Shashwat aut Mishra, Shivam Prakash aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 27(2022), 1 vom: 30. Nov., Seite 27-52 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:27 year:2022 number:1 day:30 month:11 pages:27-52 https://dx.doi.org/10.1007/s10586-022-03813-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 1 30 11 27-52 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. 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security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach |
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Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach |
abstract |
Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Abstract It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach |
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Bhattacharjee, Vandana Saket, Shashwat Mishra, Shivam Prakash |
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2024-07-04T03:22:43.647Z |
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|
score |
7.400667 |