MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE

In Neural Cryptography, Artificial Neural Networks are used for the process of key generation and encryption. Tree Parity Machine (TPM) is a single layer neural network that approaches symmetric key exchange using the process of mutual learning. This method is exploited to design a secure key exchan...
Ausführliche Beschreibung

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Autor*in:	Shikha Gupta [verfasserIn] Nalin Nanda [verfasserIn] Naman Chhikara [verfasserIn] Nishi Gupta [verfasserIn] Satbir Jain [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Neural Synchronisation Tree Parity Machine Cuckoo Search Algorithm Key Exchange Security

Übergeordnetes Werk:	In: ICTACT Journal on Soft Computing - ICT Academy of Tamil Nadu, 2014, 8(2018), 3, Seite 1663-1667
Übergeordnetes Werk:	volume:8 ; year:2018 ; number:3 ; pages:1663-1667

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.21917/ijsc.2018.0231

Katalog-ID:	DOAJ006097200

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spelling	10.21917/ijsc.2018.0231 doi (DE-627)DOAJ006097200 (DE-599)DOAJdeb4da366c044b44a7883e69d5d25c2d DE-627 ger DE-627 rakwb eng TK7885-7895 Shikha Gupta verfasserin aut MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In Neural Cryptography, Artificial Neural Networks are used for the process of key generation and encryption. Tree Parity Machine (TPM) is a single layer neural network that approaches symmetric key exchange using the process of mutual learning. This method is exploited to design a secure key exchange protocol, where the sender and the receiver TPMs are synchronized to obtain an identically tuned weight vectors in both the networks. The synchronized TPMs are then capable of generating a key stream. The time required for synchronization depends on the initial weight vectors which are randomly initialized. In the proposed method, the process of synchronization is expedited using Cuckoo Search (CS) Algorithm used for the generation of optimal weights. Neural Synchronisation Tree Parity Machine Cuckoo Search Algorithm Key Exchange Security Computer engineering. Computer hardware Nalin Nanda verfasserin aut Naman Chhikara verfasserin aut Nishi Gupta verfasserin aut Satbir Jain verfasserin aut In ICTACT Journal on Soft Computing ICT Academy of Tamil Nadu, 2014 8(2018), 3, Seite 1663-1667 (DE-627)802535402 (DE-600)2798317-1 22296956 nnns volume:8 year:2018 number:3 pages:1663-1667 https://doi.org/10.21917/ijsc.2018.0231 kostenfrei https://doaj.org/article/deb4da366c044b44a7883e69d5d25c2d kostenfrei http://ictactjournals.in/ArticleDetails.aspx?id=3427 kostenfrei https://doaj.org/toc/0976-6561 Journal toc kostenfrei https://doaj.org/toc/2229-6956 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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 2018 3 1663-1667
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allfieldsSound	10.21917/ijsc.2018.0231 doi (DE-627)DOAJ006097200 (DE-599)DOAJdeb4da366c044b44a7883e69d5d25c2d DE-627 ger DE-627 rakwb eng TK7885-7895 Shikha Gupta verfasserin aut MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In Neural Cryptography, Artificial Neural Networks are used for the process of key generation and encryption. Tree Parity Machine (TPM) is a single layer neural network that approaches symmetric key exchange using the process of mutual learning. This method is exploited to design a secure key exchange protocol, where the sender and the receiver TPMs are synchronized to obtain an identically tuned weight vectors in both the networks. The synchronized TPMs are then capable of generating a key stream. The time required for synchronization depends on the initial weight vectors which are randomly initialized. In the proposed method, the process of synchronization is expedited using Cuckoo Search (CS) Algorithm used for the generation of optimal weights. Neural Synchronisation Tree Parity Machine Cuckoo Search Algorithm Key Exchange Security Computer engineering. Computer hardware Nalin Nanda verfasserin aut Naman Chhikara verfasserin aut Nishi Gupta verfasserin aut Satbir Jain verfasserin aut In ICTACT Journal on Soft Computing ICT Academy of Tamil Nadu, 2014 8(2018), 3, Seite 1663-1667 (DE-627)802535402 (DE-600)2798317-1 22296956 nnns volume:8 year:2018 number:3 pages:1663-1667 https://doi.org/10.21917/ijsc.2018.0231 kostenfrei https://doaj.org/article/deb4da366c044b44a7883e69d5d25c2d kostenfrei http://ictactjournals.in/ArticleDetails.aspx?id=3427 kostenfrei https://doaj.org/toc/0976-6561 Journal toc kostenfrei https://doaj.org/toc/2229-6956 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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 2018 3 1663-1667
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authorswithroles_txt_mv	Shikha Gupta @@aut@@ Nalin Nanda @@aut@@ Naman Chhikara @@aut@@ Nishi Gupta @@aut@@ Satbir Jain @@aut@@
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callnumber-first	T - Technology
author	Shikha Gupta
spellingShingle	Shikha Gupta misc TK7885-7895 misc Neural Synchronisation misc Tree Parity Machine misc Cuckoo Search Algorithm misc Key Exchange misc Security misc Computer engineering. Computer hardware MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE
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topic_title	TK7885-7895 MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE Neural Synchronisation Tree Parity Machine Cuckoo Search Algorithm Key Exchange Security
topic	misc TK7885-7895 misc Neural Synchronisation misc Tree Parity Machine misc Cuckoo Search Algorithm misc Key Exchange misc Security misc Computer engineering. Computer hardware
topic_unstemmed	misc TK7885-7895 misc Neural Synchronisation misc Tree Parity Machine misc Cuckoo Search Algorithm misc Key Exchange misc Security misc Computer engineering. Computer hardware
topic_browse	misc TK7885-7895 misc Neural Synchronisation misc Tree Parity Machine misc Cuckoo Search Algorithm misc Key Exchange misc Security misc Computer engineering. Computer hardware
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title	MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE
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title_full	MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE
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title_sort	mutual learning in tree parity machines using cuckoo search algorithm for secure public key exchange
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title_auth	MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE
abstract	In Neural Cryptography, Artificial Neural Networks are used for the process of key generation and encryption. Tree Parity Machine (TPM) is a single layer neural network that approaches symmetric key exchange using the process of mutual learning. This method is exploited to design a secure key exchange protocol, where the sender and the receiver TPMs are synchronized to obtain an identically tuned weight vectors in both the networks. The synchronized TPMs are then capable of generating a key stream. The time required for synchronization depends on the initial weight vectors which are randomly initialized. In the proposed method, the process of synchronization is expedited using Cuckoo Search (CS) Algorithm used for the generation of optimal weights.
abstractGer	In Neural Cryptography, Artificial Neural Networks are used for the process of key generation and encryption. Tree Parity Machine (TPM) is a single layer neural network that approaches symmetric key exchange using the process of mutual learning. This method is exploited to design a secure key exchange protocol, where the sender and the receiver TPMs are synchronized to obtain an identically tuned weight vectors in both the networks. The synchronized TPMs are then capable of generating a key stream. The time required for synchronization depends on the initial weight vectors which are randomly initialized. In the proposed method, the process of synchronization is expedited using Cuckoo Search (CS) Algorithm used for the generation of optimal weights.
abstract_unstemmed	In Neural Cryptography, Artificial Neural Networks are used for the process of key generation and encryption. Tree Parity Machine (TPM) is a single layer neural network that approaches symmetric key exchange using the process of mutual learning. This method is exploited to design a secure key exchange protocol, where the sender and the receiver TPMs are synchronized to obtain an identically tuned weight vectors in both the networks. The synchronized TPMs are then capable of generating a key stream. The time required for synchronization depends on the initial weight vectors which are randomly initialized. In the proposed method, the process of synchronization is expedited using Cuckoo Search (CS) Algorithm used for the generation of optimal weights.
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title_short	MUTUAL LEARNING IN TREE PARITY MACHINES USING CUCKOO SEARCH ALGORITHM FOR SECURE PUBLIC KEY EXCHANGE
url	https://doi.org/10.21917/ijsc.2018.0231 https://doaj.org/article/deb4da366c044b44a7883e69d5d25c2d http://ictactjournals.in/ArticleDetails.aspx?id=3427 https://doaj.org/toc/0976-6561 https://doaj.org/toc/2229-6956
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score	7.3999014

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