HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks

Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and ne...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	S, Aruna [verfasserIn] G, Dr. Usha [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Industry4.0 S-BoX Multi core Embedded Systems Chaotic S-Box Prediction Learning Machines Side Channel Attacks (SCA) IoT network

Übergeordnetes Werk:	Enthalten in: Microprocessors and microsystems - Amsterdam [u.a.] : Elsevier, 1979, 81
Übergeordnetes Werk:	volume:81

DOI / URN:	10.1016/j.micpro.2020.103737

Katalog-ID:	ELV005909104

Internformat


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650		4	\|a IoT network
700	1		\|a G, Dr. Usha \|e verfasserin \|4 aut
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publishDate	2020
allfields	10.1016/j.micpro.2020.103737 doi (DE-627)ELV005909104 (ELSEVIER)S0141-9331(20)30882-6 DE-627 ger DE-627 rda eng 510 VZ 53.55 bkl 54.31 bkl S, Aruna verfasserin aut HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and needs to be a diagnosis for every minute. To enhance the security in the network, a mathematically secure algorithms were formulated and runs on the cryptographic embedded chips to counterfeit the risks which are caused by the different attacks such as side channel attacks (SCA) on the networks. Even though many cryptographic encryption algorithms such as AES, DES, RC4 algorithms were gaining its importance, fixed encryption keys, non-intelligent detection of attacks, cognitive countermeasures are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on design of new AES with HPAC-SBOX (Hybrid Prediction and Adaptive Chaos) which integrates powerful predictive learning algorithms and adaptive chaotic logistic S-Box. The following contributions of this research articles are: a) Preparation of Data Sets from the Power consumption traces captured from Multi Core Embedded boards while running the Advanced Encryption Systems(AES) on it b) Implementation of High Speed and High Accurate Prediction learning machines for the prediction of side-channel attacks c) Design of Adaptive Chaotic S-Box using 3-Dlogistic Hyperbolic maps for attacked bits. To evaluate the proposed architecture, experimentation in carried out in an IoT networks and various performance parameters were calculated and analyzed. The results show that the proposed architecture outperforms the other existing algorithms in terms of prediction and performance. Industry4.0 S-BoX Multi core Embedded Systems Chaotic S-Box Prediction Learning Machines Side Channel Attacks (SCA) IoT network G, Dr. Usha verfasserin aut Enthalten in Microprocessors and microsystems Amsterdam [u.a.] : Elsevier, 1979 81 Online-Ressource (DE-627)271175982 (DE-600)1479003-8 (DE-576)251938107 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.55 Mikroelektronik VZ 54.31 Rechnerarchitektur VZ AR 81
spelling	10.1016/j.micpro.2020.103737 doi (DE-627)ELV005909104 (ELSEVIER)S0141-9331(20)30882-6 DE-627 ger DE-627 rda eng 510 VZ 53.55 bkl 54.31 bkl S, Aruna verfasserin aut HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and needs to be a diagnosis for every minute. To enhance the security in the network, a mathematically secure algorithms were formulated and runs on the cryptographic embedded chips to counterfeit the risks which are caused by the different attacks such as side channel attacks (SCA) on the networks. Even though many cryptographic encryption algorithms such as AES, DES, RC4 algorithms were gaining its importance, fixed encryption keys, non-intelligent detection of attacks, cognitive countermeasures are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on design of new AES with HPAC-SBOX (Hybrid Prediction and Adaptive Chaos) which integrates powerful predictive learning algorithms and adaptive chaotic logistic S-Box. The following contributions of this research articles are: a) Preparation of Data Sets from the Power consumption traces captured from Multi Core Embedded boards while running the Advanced Encryption Systems(AES) on it b) Implementation of High Speed and High Accurate Prediction learning machines for the prediction of side-channel attacks c) Design of Adaptive Chaotic S-Box using 3-Dlogistic Hyperbolic maps for attacked bits. To evaluate the proposed architecture, experimentation in carried out in an IoT networks and various performance parameters were calculated and analyzed. The results show that the proposed architecture outperforms the other existing algorithms in terms of prediction and performance. Industry4.0 S-BoX Multi core Embedded Systems Chaotic S-Box Prediction Learning Machines Side Channel Attacks (SCA) IoT network G, Dr. Usha verfasserin aut Enthalten in Microprocessors and microsystems Amsterdam [u.a.] : Elsevier, 1979 81 Online-Ressource (DE-627)271175982 (DE-600)1479003-8 (DE-576)251938107 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.55 Mikroelektronik VZ 54.31 Rechnerarchitektur VZ AR 81
allfields_unstemmed	10.1016/j.micpro.2020.103737 doi (DE-627)ELV005909104 (ELSEVIER)S0141-9331(20)30882-6 DE-627 ger DE-627 rda eng 510 VZ 53.55 bkl 54.31 bkl S, Aruna verfasserin aut HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and needs to be a diagnosis for every minute. To enhance the security in the network, a mathematically secure algorithms were formulated and runs on the cryptographic embedded chips to counterfeit the risks which are caused by the different attacks such as side channel attacks (SCA) on the networks. Even though many cryptographic encryption algorithms such as AES, DES, RC4 algorithms were gaining its importance, fixed encryption keys, non-intelligent detection of attacks, cognitive countermeasures are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on design of new AES with HPAC-SBOX (Hybrid Prediction and Adaptive Chaos) which integrates powerful predictive learning algorithms and adaptive chaotic logistic S-Box. The following contributions of this research articles are: a) Preparation of Data Sets from the Power consumption traces captured from Multi Core Embedded boards while running the Advanced Encryption Systems(AES) on it b) Implementation of High Speed and High Accurate Prediction learning machines for the prediction of side-channel attacks c) Design of Adaptive Chaotic S-Box using 3-Dlogistic Hyperbolic maps for attacked bits. To evaluate the proposed architecture, experimentation in carried out in an IoT networks and various performance parameters were calculated and analyzed. The results show that the proposed architecture outperforms the other existing algorithms in terms of prediction and performance. Industry4.0 S-BoX Multi core Embedded Systems Chaotic S-Box Prediction Learning Machines Side Channel Attacks (SCA) IoT network G, Dr. Usha verfasserin aut Enthalten in Microprocessors and microsystems Amsterdam [u.a.] : Elsevier, 1979 81 Online-Ressource (DE-627)271175982 (DE-600)1479003-8 (DE-576)251938107 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.55 Mikroelektronik VZ 54.31 Rechnerarchitektur VZ AR 81
allfieldsGer	10.1016/j.micpro.2020.103737 doi (DE-627)ELV005909104 (ELSEVIER)S0141-9331(20)30882-6 DE-627 ger DE-627 rda eng 510 VZ 53.55 bkl 54.31 bkl S, Aruna verfasserin aut HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and needs to be a diagnosis for every minute. To enhance the security in the network, a mathematically secure algorithms were formulated and runs on the cryptographic embedded chips to counterfeit the risks which are caused by the different attacks such as side channel attacks (SCA) on the networks. Even though many cryptographic encryption algorithms such as AES, DES, RC4 algorithms were gaining its importance, fixed encryption keys, non-intelligent detection of attacks, cognitive countermeasures are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on design of new AES with HPAC-SBOX (Hybrid Prediction and Adaptive Chaos) which integrates powerful predictive learning algorithms and adaptive chaotic logistic S-Box. The following contributions of this research articles are: a) Preparation of Data Sets from the Power consumption traces captured from Multi Core Embedded boards while running the Advanced Encryption Systems(AES) on it b) Implementation of High Speed and High Accurate Prediction learning machines for the prediction of side-channel attacks c) Design of Adaptive Chaotic S-Box using 3-Dlogistic Hyperbolic maps for attacked bits. To evaluate the proposed architecture, experimentation in carried out in an IoT networks and various performance parameters were calculated and analyzed. The results show that the proposed architecture outperforms the other existing algorithms in terms of prediction and performance. Industry4.0 S-BoX Multi core Embedded Systems Chaotic S-Box Prediction Learning Machines Side Channel Attacks (SCA) IoT network G, Dr. Usha verfasserin aut Enthalten in Microprocessors and microsystems Amsterdam [u.a.] : Elsevier, 1979 81 Online-Ressource (DE-627)271175982 (DE-600)1479003-8 (DE-576)251938107 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.55 Mikroelektronik VZ 54.31 Rechnerarchitektur VZ AR 81
allfieldsSound	10.1016/j.micpro.2020.103737 doi (DE-627)ELV005909104 (ELSEVIER)S0141-9331(20)30882-6 DE-627 ger DE-627 rda eng 510 VZ 53.55 bkl 54.31 bkl S, Aruna verfasserin aut HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and needs to be a diagnosis for every minute. To enhance the security in the network, a mathematically secure algorithms were formulated and runs on the cryptographic embedded chips to counterfeit the risks which are caused by the different attacks such as side channel attacks (SCA) on the networks. Even though many cryptographic encryption algorithms such as AES, DES, RC4 algorithms were gaining its importance, fixed encryption keys, non-intelligent detection of attacks, cognitive countermeasures are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on design of new AES with HPAC-SBOX (Hybrid Prediction and Adaptive Chaos) which integrates powerful predictive learning algorithms and adaptive chaotic logistic S-Box. The following contributions of this research articles are: a) Preparation of Data Sets from the Power consumption traces captured from Multi Core Embedded boards while running the Advanced Encryption Systems(AES) on it b) Implementation of High Speed and High Accurate Prediction learning machines for the prediction of side-channel attacks c) Design of Adaptive Chaotic S-Box using 3-Dlogistic Hyperbolic maps for attacked bits. To evaluate the proposed architecture, experimentation in carried out in an IoT networks and various performance parameters were calculated and analyzed. The results show that the proposed architecture outperforms the other existing algorithms in terms of prediction and performance. Industry4.0 S-BoX Multi core Embedded Systems Chaotic S-Box Prediction Learning Machines Side Channel Attacks (SCA) IoT network G, Dr. Usha verfasserin aut Enthalten in Microprocessors and microsystems Amsterdam [u.a.] : Elsevier, 1979 81 Online-Ressource (DE-627)271175982 (DE-600)1479003-8 (DE-576)251938107 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.55 Mikroelektronik VZ 54.31 Rechnerarchitektur VZ AR 81
language	English
source	Enthalten in Microprocessors and microsystems 81 volume:81
sourceStr	Enthalten in Microprocessors and microsystems 81 volume:81
format_phy_str_mv	Article
bklname	Mikroelektronik Rechnerarchitektur
institution	findex.gbv.de
topic_facet	Industry4.0 S-BoX Multi core Embedded Systems Chaotic S-Box Prediction Learning Machines Side Channel Attacks (SCA) IoT network
dewey-raw	510
isfreeaccess_bool	false
container_title	Microprocessors and microsystems
authorswithroles_txt_mv	S, Aruna @@aut@@ G, Dr. Usha @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	271175982
dewey-sort	3510
id	ELV005909104
language_de	englisch
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author	S, Aruna
spellingShingle	S, Aruna ddc 510 bkl 53.55 bkl 54.31 misc Industry4.0 misc S-BoX misc Multi core Embedded Systems misc Chaotic S-Box misc Prediction Learning Machines misc Side Channel Attacks (SCA) misc IoT network HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks
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topic_title	510 VZ 53.55 bkl 54.31 bkl HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks Industry4.0 S-BoX Multi core Embedded Systems Chaotic S-Box Prediction Learning Machines Side Channel Attacks (SCA) IoT network
topic	ddc 510 bkl 53.55 bkl 54.31 misc Industry4.0 misc S-BoX misc Multi core Embedded Systems misc Chaotic S-Box misc Prediction Learning Machines misc Side Channel Attacks (SCA) misc IoT network
topic_unstemmed	ddc 510 bkl 53.55 bkl 54.31 misc Industry4.0 misc S-BoX misc Multi core Embedded Systems misc Chaotic S-Box misc Prediction Learning Machines misc Side Channel Attacks (SCA) misc IoT network
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title	HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks
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title_full	HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks
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author_browse	S, Aruna G, Dr. Usha
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title_sort	hpac-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an iot networks
title_auth	HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks
abstract	Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and needs to be a diagnosis for every minute. To enhance the security in the network, a mathematically secure algorithms were formulated and runs on the cryptographic embedded chips to counterfeit the risks which are caused by the different attacks such as side channel attacks (SCA) on the networks. Even though many cryptographic encryption algorithms such as AES, DES, RC4 algorithms were gaining its importance, fixed encryption keys, non-intelligent detection of attacks, cognitive countermeasures are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on design of new AES with HPAC-SBOX (Hybrid Prediction and Adaptive Chaos) which integrates powerful predictive learning algorithms and adaptive chaotic logistic S-Box. The following contributions of this research articles are: a) Preparation of Data Sets from the Power consumption traces captured from Multi Core Embedded boards while running the Advanced Encryption Systems(AES) on it b) Implementation of High Speed and High Accurate Prediction learning machines for the prediction of side-channel attacks c) Design of Adaptive Chaotic S-Box using 3-Dlogistic Hyperbolic maps for attacked bits. To evaluate the proposed architecture, experimentation in carried out in an IoT networks and various performance parameters were calculated and analyzed. The results show that the proposed architecture outperforms the other existing algorithms in terms of prediction and performance.
abstractGer	Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and needs to be a diagnosis for every minute. To enhance the security in the network, a mathematically secure algorithms were formulated and runs on the cryptographic embedded chips to counterfeit the risks which are caused by the different attacks such as side channel attacks (SCA) on the networks. Even though many cryptographic encryption algorithms such as AES, DES, RC4 algorithms were gaining its importance, fixed encryption keys, non-intelligent detection of attacks, cognitive countermeasures are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on design of new AES with HPAC-SBOX (Hybrid Prediction and Adaptive Chaos) which integrates powerful predictive learning algorithms and adaptive chaotic logistic S-Box. The following contributions of this research articles are: a) Preparation of Data Sets from the Power consumption traces captured from Multi Core Embedded boards while running the Advanced Encryption Systems(AES) on it b) Implementation of High Speed and High Accurate Prediction learning machines for the prediction of side-channel attacks c) Design of Adaptive Chaotic S-Box using 3-Dlogistic Hyperbolic maps for attacked bits. To evaluate the proposed architecture, experimentation in carried out in an IoT networks and various performance parameters were calculated and analyzed. The results show that the proposed architecture outperforms the other existing algorithms in terms of prediction and performance.
abstract_unstemmed	Today, embedded systems are augmented with the Internet of things and more with the artificial intelligence to make world even connected with aliens. With an IoT networks are getting its insight since it deals with large number of data information, security has considered to be more important and needs to be a diagnosis for every minute. To enhance the security in the network, a mathematically secure algorithms were formulated and runs on the cryptographic embedded chips to counterfeit the risks which are caused by the different attacks such as side channel attacks (SCA) on the networks. Even though many cryptographic encryption algorithms such as AES, DES, RC4 algorithms were gaining its importance, fixed encryption keys, non-intelligent detection of attacks, cognitive countermeasures are some of the real-time challenges in an existing system of encryption. Following the limitations of existing systems, this research article focuses on design of new AES with HPAC-SBOX (Hybrid Prediction and Adaptive Chaos) which integrates powerful predictive learning algorithms and adaptive chaotic logistic S-Box. The following contributions of this research articles are: a) Preparation of Data Sets from the Power consumption traces captured from Multi Core Embedded boards while running the Advanced Encryption Systems(AES) on it b) Implementation of High Speed and High Accurate Prediction learning machines for the prediction of side-channel attacks c) Design of Adaptive Chaotic S-Box using 3-Dlogistic Hyperbolic maps for attacked bits. To evaluate the proposed architecture, experimentation in carried out in an IoT networks and various performance parameters were calculated and analyzed. The results show that the proposed architecture outperforms the other existing algorithms in terms of prediction and performance.
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title_short	HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks
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up_date	2024-07-06T19:34:42.417Z
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HPAC-sbox- a novel implementation of predictive learning classifier and adaptive chaotic s-box for counterfeiting sidechannel attacks in an IOT networks

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