Digital forensic analysis of intelligent and smart IoT devices
Abstract AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a...
Ausführliche Beschreibung
Autor*in: |
Kim, Minju [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Springer US, 1987, 79(2022), 1 vom: 20. Juli, Seite 973-997 |
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Übergeordnetes Werk: |
volume:79 ; year:2022 ; number:1 ; day:20 ; month:07 ; pages:973-997 |
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DOI / URN: |
10.1007/s11227-022-04639-5 |
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OLC208023031X |
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10.1007/s11227-022-04639-5 doi (DE-627)OLC208023031X (DE-He213)s11227-022-04639-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Kim, Minju verfasserin aut Digital forensic analysis of intelligent and smart IoT devices 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a range of services to users. As the generated data are preserved in the storage of the wearable device, getting access to these data from the device can prove useful in criminal investigations. We, therefore, propose a forensic model based on direct connections using wireless or interfaces beyond indirect forensics for wearable devices. The forensic model was derived based on the ecosystem of wearable devices and was divided into logical and physical forensic methods. To confirm the applicability of the forensic model, we applied it to wearable devices from Samsung, Apple, and Garmin. Our results demonstrate that the proposed forensic model can be successfully used to derive artifacts. Digital forensics Wearable ecosystem Smartwatch Smartband Shin, Yeonghun aut Jo, Wooyeon aut Shon, Taeshik aut Enthalten in The journal of supercomputing Springer US, 1987 79(2022), 1 vom: 20. Juli, Seite 973-997 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2022 number:1 day:20 month:07 pages:973-997 https://doi.org/10.1007/s11227-022-04639-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2022 1 20 07 973-997 |
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10.1007/s11227-022-04639-5 doi (DE-627)OLC208023031X (DE-He213)s11227-022-04639-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Kim, Minju verfasserin aut Digital forensic analysis of intelligent and smart IoT devices 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a range of services to users. As the generated data are preserved in the storage of the wearable device, getting access to these data from the device can prove useful in criminal investigations. We, therefore, propose a forensic model based on direct connections using wireless or interfaces beyond indirect forensics for wearable devices. The forensic model was derived based on the ecosystem of wearable devices and was divided into logical and physical forensic methods. To confirm the applicability of the forensic model, we applied it to wearable devices from Samsung, Apple, and Garmin. Our results demonstrate that the proposed forensic model can be successfully used to derive artifacts. Digital forensics Wearable ecosystem Smartwatch Smartband Shin, Yeonghun aut Jo, Wooyeon aut Shon, Taeshik aut Enthalten in The journal of supercomputing Springer US, 1987 79(2022), 1 vom: 20. Juli, Seite 973-997 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2022 number:1 day:20 month:07 pages:973-997 https://doi.org/10.1007/s11227-022-04639-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2022 1 20 07 973-997 |
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10.1007/s11227-022-04639-5 doi (DE-627)OLC208023031X (DE-He213)s11227-022-04639-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Kim, Minju verfasserin aut Digital forensic analysis of intelligent and smart IoT devices 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a range of services to users. As the generated data are preserved in the storage of the wearable device, getting access to these data from the device can prove useful in criminal investigations. We, therefore, propose a forensic model based on direct connections using wireless or interfaces beyond indirect forensics for wearable devices. The forensic model was derived based on the ecosystem of wearable devices and was divided into logical and physical forensic methods. To confirm the applicability of the forensic model, we applied it to wearable devices from Samsung, Apple, and Garmin. Our results demonstrate that the proposed forensic model can be successfully used to derive artifacts. Digital forensics Wearable ecosystem Smartwatch Smartband Shin, Yeonghun aut Jo, Wooyeon aut Shon, Taeshik aut Enthalten in The journal of supercomputing Springer US, 1987 79(2022), 1 vom: 20. Juli, Seite 973-997 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2022 number:1 day:20 month:07 pages:973-997 https://doi.org/10.1007/s11227-022-04639-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2022 1 20 07 973-997 |
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10.1007/s11227-022-04639-5 doi (DE-627)OLC208023031X (DE-He213)s11227-022-04639-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Kim, Minju verfasserin aut Digital forensic analysis of intelligent and smart IoT devices 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a range of services to users. As the generated data are preserved in the storage of the wearable device, getting access to these data from the device can prove useful in criminal investigations. We, therefore, propose a forensic model based on direct connections using wireless or interfaces beyond indirect forensics for wearable devices. The forensic model was derived based on the ecosystem of wearable devices and was divided into logical and physical forensic methods. To confirm the applicability of the forensic model, we applied it to wearable devices from Samsung, Apple, and Garmin. Our results demonstrate that the proposed forensic model can be successfully used to derive artifacts. Digital forensics Wearable ecosystem Smartwatch Smartband Shin, Yeonghun aut Jo, Wooyeon aut Shon, Taeshik aut Enthalten in The journal of supercomputing Springer US, 1987 79(2022), 1 vom: 20. Juli, Seite 973-997 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2022 number:1 day:20 month:07 pages:973-997 https://doi.org/10.1007/s11227-022-04639-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2022 1 20 07 973-997 |
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Abstract AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a range of services to users. As the generated data are preserved in the storage of the wearable device, getting access to these data from the device can prove useful in criminal investigations. We, therefore, propose a forensic model based on direct connections using wireless or interfaces beyond indirect forensics for wearable devices. The forensic model was derived based on the ecosystem of wearable devices and was divided into logical and physical forensic methods. To confirm the applicability of the forensic model, we applied it to wearable devices from Samsung, Apple, and Garmin. Our results demonstrate that the proposed forensic model can be successfully used to derive artifacts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a range of services to users. As the generated data are preserved in the storage of the wearable device, getting access to these data from the device can prove useful in criminal investigations. We, therefore, propose a forensic model based on direct connections using wireless or interfaces beyond indirect forensics for wearable devices. The forensic model was derived based on the ecosystem of wearable devices and was divided into logical and physical forensic methods. To confirm the applicability of the forensic model, we applied it to wearable devices from Samsung, Apple, and Garmin. Our results demonstrate that the proposed forensic model can be successfully used to derive artifacts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a range of services to users. As the generated data are preserved in the storage of the wearable device, getting access to these data from the device can prove useful in criminal investigations. We, therefore, propose a forensic model based on direct connections using wireless or interfaces beyond indirect forensics for wearable devices. The forensic model was derived based on the ecosystem of wearable devices and was divided into logical and physical forensic methods. To confirm the applicability of the forensic model, we applied it to wearable devices from Samsung, Apple, and Garmin. Our results demonstrate that the proposed forensic model can be successfully used to derive artifacts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC208023031X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506143944.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230131s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11227-022-04639-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC208023031X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11227-022-04639-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kim, Minju</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Digital forensic analysis of intelligent and smart IoT devices</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract AI is combined with various devices to provide improved performance. IoT devices combined with AI are called smart IoT. Smart IoT devices can be controlled using wearable devices. Wearable devices such as smartwatches and smartbands generate personal information through sensors to provide a range of services to users. As the generated data are preserved in the storage of the wearable device, getting access to these data from the device can prove useful in criminal investigations. We, therefore, propose a forensic model based on direct connections using wireless or interfaces beyond indirect forensics for wearable devices. The forensic model was derived based on the ecosystem of wearable devices and was divided into logical and physical forensic methods. To confirm the applicability of the forensic model, we applied it to wearable devices from Samsung, Apple, and Garmin. 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