MLP and CNN-based Classification of Points of Interest in Side-channel Attacks

Abstract A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positi...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Feng, Hanwen [verfasserIn] Lin, Weiguo Shang, Wenqian Cao, Jianxiang Huang, Wei

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Point of interest forward difference trace multi-layer perceptron convolutional neural network

Anmerkung:	© The Authors 2020

Übergeordnetes Werk:	Enthalten in: International Journal of Networked and Distributed Computing - Springer Netherlands, 2022, 8(2020), 2 vom: März, Seite 108-117
Übergeordnetes Werk:	volume:8 ; year:2020 ; number:2 ; month:03 ; pages:108-117

Links:	Volltext

DOI / URN:	10.2991/ijndc.k.200326.001

Katalog-ID:	SPR05457837X

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520			\|a Abstract A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation.
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allfields	10.2991/ijndc.k.200326.001 doi (DE-627)SPR05457837X (SPR)ijndc.k.200326.001-e DE-627 ger DE-627 rakwb eng Feng, Hanwen verfasserin aut MLP and CNN-based Classification of Points of Interest in Side-channel Attacks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2020 Abstract A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation. Point of interest (dpeaa)DE-He213 forward difference (dpeaa)DE-He213 trace (dpeaa)DE-He213 multi-layer perceptron (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213 Lin, Weiguo aut Shang, Wenqian aut Cao, Jianxiang aut Huang, Wei aut Enthalten in International Journal of Networked and Distributed Computing Springer Netherlands, 2022 8(2020), 2 vom: März, Seite 108-117 (DE-627)1006076743 2211-7946 nnns volume:8 year:2020 number:2 month:03 pages:108-117 https://dx.doi.org/10.2991/ijndc.k.200326.001 kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 2 03 108-117
spelling	10.2991/ijndc.k.200326.001 doi (DE-627)SPR05457837X (SPR)ijndc.k.200326.001-e DE-627 ger DE-627 rakwb eng Feng, Hanwen verfasserin aut MLP and CNN-based Classification of Points of Interest in Side-channel Attacks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2020 Abstract A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation. Point of interest (dpeaa)DE-He213 forward difference (dpeaa)DE-He213 trace (dpeaa)DE-He213 multi-layer perceptron (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213 Lin, Weiguo aut Shang, Wenqian aut Cao, Jianxiang aut Huang, Wei aut Enthalten in International Journal of Networked and Distributed Computing Springer Netherlands, 2022 8(2020), 2 vom: März, Seite 108-117 (DE-627)1006076743 2211-7946 nnns volume:8 year:2020 number:2 month:03 pages:108-117 https://dx.doi.org/10.2991/ijndc.k.200326.001 kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 2 03 108-117
allfields_unstemmed	10.2991/ijndc.k.200326.001 doi (DE-627)SPR05457837X (SPR)ijndc.k.200326.001-e DE-627 ger DE-627 rakwb eng Feng, Hanwen verfasserin aut MLP and CNN-based Classification of Points of Interest in Side-channel Attacks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2020 Abstract A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation. Point of interest (dpeaa)DE-He213 forward difference (dpeaa)DE-He213 trace (dpeaa)DE-He213 multi-layer perceptron (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213 Lin, Weiguo aut Shang, Wenqian aut Cao, Jianxiang aut Huang, Wei aut Enthalten in International Journal of Networked and Distributed Computing Springer Netherlands, 2022 8(2020), 2 vom: März, Seite 108-117 (DE-627)1006076743 2211-7946 nnns volume:8 year:2020 number:2 month:03 pages:108-117 https://dx.doi.org/10.2991/ijndc.k.200326.001 kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 2 03 108-117
allfieldsGer	10.2991/ijndc.k.200326.001 doi (DE-627)SPR05457837X (SPR)ijndc.k.200326.001-e DE-627 ger DE-627 rakwb eng Feng, Hanwen verfasserin aut MLP and CNN-based Classification of Points of Interest in Side-channel Attacks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2020 Abstract A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation. Point of interest (dpeaa)DE-He213 forward difference (dpeaa)DE-He213 trace (dpeaa)DE-He213 multi-layer perceptron (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213 Lin, Weiguo aut Shang, Wenqian aut Cao, Jianxiang aut Huang, Wei aut Enthalten in International Journal of Networked and Distributed Computing Springer Netherlands, 2022 8(2020), 2 vom: März, Seite 108-117 (DE-627)1006076743 2211-7946 nnns volume:8 year:2020 number:2 month:03 pages:108-117 https://dx.doi.org/10.2991/ijndc.k.200326.001 kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 2 03 108-117
allfieldsSound	10.2991/ijndc.k.200326.001 doi (DE-627)SPR05457837X (SPR)ijndc.k.200326.001-e DE-627 ger DE-627 rakwb eng Feng, Hanwen verfasserin aut MLP and CNN-based Classification of Points of Interest in Side-channel Attacks 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2020 Abstract A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation. Point of interest (dpeaa)DE-He213 forward difference (dpeaa)DE-He213 trace (dpeaa)DE-He213 multi-layer perceptron (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213 Lin, Weiguo aut Shang, Wenqian aut Cao, Jianxiang aut Huang, Wei aut Enthalten in International Journal of Networked and Distributed Computing Springer Netherlands, 2022 8(2020), 2 vom: März, Seite 108-117 (DE-627)1006076743 2211-7946 nnns volume:8 year:2020 number:2 month:03 pages:108-117 https://dx.doi.org/10.2991/ijndc.k.200326.001 kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 2 03 108-117
language	English
source	Enthalten in International Journal of Networked and Distributed Computing 8(2020), 2 vom: März, Seite 108-117 volume:8 year:2020 number:2 month:03 pages:108-117
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authorswithroles_txt_mv	Feng, Hanwen @@aut@@ Lin, Weiguo @@aut@@ Shang, Wenqian @@aut@@ Cao, Jianxiang @@aut@@ Huang, Wei @@aut@@
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author	Feng, Hanwen
spellingShingle	Feng, Hanwen misc Point of interest misc forward difference misc trace misc multi-layer perceptron misc convolutional neural network MLP and CNN-based Classification of Points of Interest in Side-channel Attacks
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topic_title	MLP and CNN-based Classification of Points of Interest in Side-channel Attacks Point of interest (dpeaa)DE-He213 forward difference (dpeaa)DE-He213 trace (dpeaa)DE-He213 multi-layer perceptron (dpeaa)DE-He213 convolutional neural network (dpeaa)DE-He213
topic	misc Point of interest misc forward difference misc trace misc multi-layer perceptron misc convolutional neural network
topic_unstemmed	misc Point of interest misc forward difference misc trace misc multi-layer perceptron misc convolutional neural network
topic_browse	misc Point of interest misc forward difference misc trace misc multi-layer perceptron misc convolutional neural network
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	MLP and CNN-based Classification of Points of Interest in Side-channel Attacks
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title_full	MLP and CNN-based Classification of Points of Interest in Side-channel Attacks
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journal	International Journal of Networked and Distributed Computing
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author_browse	Feng, Hanwen Lin, Weiguo Shang, Wenqian Cao, Jianxiang Huang, Wei
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author-letter	Feng, Hanwen
doi_str_mv	10.2991/ijndc.k.200326.001
title_sort	mlp and cnn-based classification of points of interest in side-channel attacks
title_auth	MLP and CNN-based Classification of Points of Interest in Side-channel Attacks
abstract	Abstract A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation. © The Authors 2020
abstractGer	Abstract A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation. © The Authors 2020
abstract_unstemmed	Abstract A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation. © The Authors 2020
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title_short	MLP and CNN-based Classification of Points of Interest in Side-channel Attacks
url	https://dx.doi.org/10.2991/ijndc.k.200326.001
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author2	Lin, Weiguo Shang, Wenqian Cao, Jianxiang Huang, Wei
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up_date	2024-07-04T02:14:58.395Z
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