{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T05:14:59Z","timestamp":1751951699938,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T00:00:00Z","timestamp":1619654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T00:00:00Z","timestamp":1619654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Synchronization of two neural networks through mutual learning is used to exchange the key over a public channel. In the absence of a weight vector from another party, the key challenge with neural synchronization is how to assess the coordination of two communication parties. There is an issue of delay in the current techniques in the synchronization assessment that has an impact on the security and privacy of the neural synchronization. In this paper, to assess the complete coordination of a cluster of neural networks more efficiently and timely, an important strategy for assessing coordination is presented. To approximately determine the degree of synchronization, the frequency of the two networks having the same output in prior iterations is used. The hash is used to determine if both the networks are completely synchronized exactly when a certain threshold is crossed. The improved technique makes absolute coordination between two communication parties using the weight vectors\u2019 has value. In contrast, with existing approaches, two communicating parties who follow the proposed approach will detect complete synchronization sooner. This reduces the effective geometric likelihood. The proposed method, therefore, increases the safety of the protocol for neural key exchange. This proposed technique has been passed through different parametric tests. Simulations of the process show effectiveness in terms of cited results in the paper.<\/jats:p>","DOI":"10.1007\/s40747-021-00344-7","type":"journal-article","created":{"date-parts":[[2021,4,29]],"date-time":"2021-04-29T16:02:31Z","timestamp":1619712151000},"page":"307-321","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mutual learning-based efficient synchronization of neural networks to exchange the neural key"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4951-4729","authenticated-orcid":false,"given":"Arindam","family":"Sarkar","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,29]]},"reference":[{"issue":"5","key":"344_CR1","doi-asserted-by":"publisher","first-page":"230","DOI":"10.32628\/ijsrset196550","volume":"6","author":"ZK Abdalrdha","year":"2019","unstructured":"Abdalrdha ZK, AL-Qinani IH, Abbas FN (2019) Subject review: key generation in different cryptography algorithm. Int J Res Sci Eng Technol 6(5):230\u2013240. https:\/\/doi.org\/10.32628\/ijsrset196550","journal-title":"Int J Res Sci Eng Technol"},{"key":"344_CR2","doi-asserted-by":"crossref","unstructured":"Alani M (2019) Applications of machine learning in cryptography: a survey. In: Proceedings of the 3rd International Conference on cryptography, security and privacy, pp 23\u201327","DOI":"10.1145\/3309074.3309092"},{"issue":"11","key":"344_CR3","first-page":"2878","volume":"9","author":"M Alazab","year":"2014","unstructured":"Alazab M, Huda S, Abawajy J, Islam R, Yearwood J, Venkatraman S, Broadhurst R (2014) A hybrid wrapper-filter approach for malware detection. J Netw 9(11):2878\u20132891","journal-title":"J Netw"},{"key":"344_CR4","doi-asserted-by":"publisher","first-page":"85454","DOI":"10.1109\/ACCESS.2020.2991067","volume":"8","author":"M Alazab","year":"2020","unstructured":"Alazab M, Khan S, Krishnan SSR, Pham Q, Reddy MPK, Gadekallu TR (2020) A multidirectional LSTM model for predicting the stability of a smart grid. IEEE Access 8:85454\u201385463. https:\/\/doi.org\/10.1109\/ACCESS.2020.2991067","journal-title":"IEEE Access"},{"key":"344_CR5","doi-asserted-by":"crossref","unstructured":"Allam AM, Abbas HM, El-Kharashi MW (2013) Authenticated key exchange protocol using neural cryptography with secret boundaries. In: Proceedings of the 2013 International Joint Conference on neural networks, IJCNN 2013, pp 1\u20138","DOI":"10.1109\/IJCNN.2013.6707125"},{"issue":"2","key":"344_CR6","doi-asserted-by":"publisher","first-page":"219","DOI":"10.3390\/electronics9020219","volume":"9","author":"SSSRK Bhattacharya","year":"2020","unstructured":"Bhattacharya SSSRK, Maddikunta PKR, Kaluri R, Singh S, Gadekallu TR, Alazab M, Tariq U (2020) A novel PCA-firefly based XGBoost classification model for intrusion detection in networks using GPU. Electronics 9(2):219. https:\/\/doi.org\/10.3390\/electronics9020219","journal-title":"Electronics"},{"key":"344_CR7","first-page":"3140","volume":"8","author":"S Chourasia","year":"2019","unstructured":"Chourasia S, Bharadwaj HC, Das Q, Agarwal K, Lavanya K (2019) Vectorized neural key exchange using tree parity machine. Compusoft 8:3140\u20133145","journal-title":"Compusoft"},{"key":"344_CR8","doi-asserted-by":"crossref","unstructured":"Desai V, Deshmukh V, Rao D (2011) Pseudo random number generator using Elman neural network. In: Recent Advances in Intelligent Computational Systems (RAICS) pp 251\u2013254","DOI":"10.1109\/RAICS.2011.6069312"},{"key":"344_CR9","doi-asserted-by":"publisher","unstructured":"Dolecki M, Kozera R (2015) The impact of the TPM weights distribution on network synchronization time. In: Saeed K, Homenda W (eds) Computer information systems and industrial management. CISIM 2015. Lecture Notes in Computer Science, vol 9339. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-24369-6_373","DOI":"10.1007\/978-3-319-24369-6_373"},{"issue":"11","key":"344_CR10","doi-asserted-by":"publisher","first-page":"4999","DOI":"10.1109\/TNNLS.2019.2955165","volume":"31","author":"T Dong","year":"2020","unstructured":"Dong T, Huang T (2020) Neural cryptography based on complex-valued neural network. IEEE Trans Neural Netw Learn Syst 31(11):4999\u20135004. https:\/\/doi.org\/10.1109\/TNNLS.2019.2955165","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"344_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-020-00987-8","author":"TR Gadekallu","year":"2020","unstructured":"Gadekallu TR, Rajput DS, Reddy MPK, Lakshmanna K, Bhattacharya S, Singh S, Jolfaei A, Alazab M (2020) A novel PCA-whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. J Real-Time Image Proc. https:\/\/doi.org\/10.1007\/s11554-020-00987-8","journal-title":"J Real-Time Image Proc"},{"key":"344_CR12","doi-asserted-by":"crossref","unstructured":"Hadke PP, Kale SG (2016) Use of neural networks in cryptography: a review. In: Proceedings of the 2016 World Conference on futuristic trends in research and innovation for social welfare (Startup Conclave), pp 1\u20134","DOI":"10.1109\/STARTUP.2016.7583925"},{"issue":"5","key":"344_CR13","doi-asserted-by":"publisher","first-page":"2557","DOI":"10.1016\/j.chaos.2007.10.049","volume":"40","author":"A Kanso","year":"2009","unstructured":"Kanso A, Smaoui N (2009) Logistic chaotic maps for binary numbers generations. Chaos Solitons Fractals 40(5):2557\u20132568. https:\/\/doi.org\/10.1016\/j.chaos.2007.10.049","journal-title":"Chaos Solitons Fractals"},{"issue":"1","key":"344_CR14","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1209\/epl\/i2002-00552-9","volume":"57","author":"I Kanter","year":"2002","unstructured":"Kanter I, Kinzel W, Kanter E (2002) Secure exchange of information by synchronization of neural networks. Europhys Lett (EPL) 57(1):141\u2013147. https:\/\/doi.org\/10.1209\/epl\/i2002-00552-9","journal-title":"Europhys Lett (EPL)"},{"key":"344_CR15","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.chaos.2018.12.021","volume":"119","author":"B Karakaya","year":"2019","unstructured":"Karakaya B, G\u00fclten A, Frasca M (2019) A true random bit generator based on a memristive chaotic circuit: analysis, design and FPGA implementation. Chaos Solitons Fractals 119:143\u2013149","journal-title":"Chaos Solitons Fractals"},{"key":"344_CR16","doi-asserted-by":"crossref","unstructured":"Klimov A, Mityagin A, Shamir A (2002) Analysis of neural cryptography. In: Proceedings of the 8th International Conference on the theory and application of cryptology and information security, pp 288\u2013298","DOI":"10.1007\/3-540-36178-2_18"},{"issue":"10","key":"344_CR17","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1049\/iet-ifs.2014.0192","volume":"2","author":"L Liu","year":"2016","unstructured":"Liu L, Miao S, Hu H, Deng Y (2016) Pseudo-random bit generator based on non-stationary logistic maps. IET Inf Secur 2(10):87\u201394","journal-title":"IET Inf Secur"},{"issue":"8","key":"344_CR18","doi-asserted-by":"publisher","first-page":"2358","DOI":"10.1109\/TNNLS.2018.2884620","volume":"30","author":"P Liu","year":"2019","unstructured":"Liu P, Zeng Z, Wang J (2019) Global synchronization of coupled fractional-order recurrent neural networks. IEEE Trans Neural Netw Learn Syst 30(8):2358\u20132368","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"#cr-split#-344_CR19.1","doi-asserted-by":"crossref","unstructured":"Mandal J, Sarkar A (2012) Neural weight session key based encryption for online wireless communication (NWSKE). In: Mandal J","DOI":"10.1109\/ReTIS.2011.6146841"},{"key":"#cr-split#-344_CR19.2","unstructured":"(ed) Research and Higher Education in Computer Science and Information Technology, (RHECSIT- 2012), pp 90-95"},{"key":"344_CR20","doi-asserted-by":"crossref","unstructured":"Mehic M, Niemiec H, Siljak M, Voznak (2020) Error reconciliation in quantum key distribution protocols. In: Proceedings of the International Conference on reversible computation, pp 222\u2013236","DOI":"10.1007\/978-3-030-47361-7_11"},{"key":"344_CR21","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/s11128-019-2296-4","volume":"18","author":"Niemiec","year":"2019","unstructured":"Niemiec (2019) Error correction in quantum cryptography based on artificial neural networks. Quantum Inf Process 8:174\u2013174","journal-title":"Quantum Inf Process"},{"key":"344_CR22","doi-asserted-by":"crossref","unstructured":"Niemiec M, Mehic M, Voznak (2018) Security verification of artificial neural networks used to error correction in quantum cryptography. In: Proceedings of the 26th Telecommunications Forum (TELFOR), pp 1\u20134","DOI":"10.1109\/TELFOR.2018.8612006"},{"key":"344_CR23","unstructured":"NIST (2020) NIST statistical test. http:\/\/csrc.nist.gov\/groups\/ST\/toolkit\/rng\/stats_tests.html. Accessed 28 Nov 2020"},{"issue":"10","key":"344_CR24","first-page":"45","volume":"11","author":"SK Pal","year":"2019","unstructured":"Pal SK, Mishra S, Mishra S (2019) An TPM based approach for generation of secret key. Int J Comput Netw Inf Secur 11(10):45\u201350","journal-title":"Int J Comput Netw Inf Secur"},{"key":"344_CR25","first-page":"441","volume":"33","author":"V Patidar","year":"2009","unstructured":"Patidar V, Sud KK, Pareek NK (2009) A pseudo random bit generator based on chaotic logistic map and its statistical testing. Informatica 33:441\u2013452","journal-title":"Informatica"},{"issue":"2","key":"344_CR26","doi-asserted-by":"publisher","first-page":"483","DOI":"10.5937\/vojtehg64-8877","volume":"64","author":"D Protic","year":"2016","unstructured":"Protic D (2016) Neural cryptography. Vojnotehnicki glasnik 64(2):483\u2013495. https:\/\/doi.org\/10.5937\/vojtehg64-8877","journal-title":"Vojnotehnicki glasnik"},{"key":"344_CR27","volume-title":"Neural synchronization and cryptography","author":"A Ruttor","year":"2006","unstructured":"Ruttor A (2006) Neural synchronization and cryptography. University of St Gallen, St. Gallen"},{"key":"344_CR28","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.73.036121","author":"A Ruttor","year":"2006","unstructured":"Ruttor A, Kinzel W, Naeh R, Kanter I (2006) Genetic attack on neural cryptography. Phys Rev E. https:\/\/doi.org\/10.1103\/physreve.73.036121","journal-title":"Phys Rev E"},{"key":"344_CR29","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.75.056104","author":"A Ruttor","year":"2007","unstructured":"Ruttor A, Kinzel W, Kanter I (2007) Dynamics of neural cryptography. Phys Rev E. https:\/\/doi.org\/10.1103\/physreve.75.056104","journal-title":"Phys Rev E"},{"key":"344_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/8214681","volume":"2019","author":"D Salguero \u00c9dgar","year":"2019","unstructured":"Salguero \u00c9dgar D, Fuertes W, Lascano E (2019) On the development of an optimal structure of tree parity machine for the establishment of a cryptographic key. Secur Commun Netw 2019:1\u201310. https:\/\/doi.org\/10.1155\/2019\/8214681","journal-title":"Secur Commun Netw"},{"key":"344_CR31","doi-asserted-by":"crossref","unstructured":"Santhanalakshmi S, Sudarshan T, Patra GK (2014) Neural synchronization by mutual learning using genetic approach for secure key generation. In: Proceedings of the International Conference on security in computer networks and distributed systems, pp 422\u2013431","DOI":"10.1007\/978-3-642-34135-9_41"},{"key":"344_CR32","doi-asserted-by":"crossref","unstructured":"Santhanalakshmi S, Sangeeta K, Patra GK (2015) Analysis of neural synchronization using genetic approach for secure key generation. Commun Comput Inf Sci 536:207\u2013216","DOI":"10.1007\/978-3-319-22915-7_20"},{"issue":"1","key":"344_CR33","first-page":"44","volume":"8","author":"A Sarkar","year":"2019","unstructured":"Sarkar A (2019) Multilayer neural network synchronized secured session key based encryption in wireless communication. Int J Artif Intell 8(1):44\u201353","journal-title":"Int J Artif Intell"},{"issue":"10","key":"344_CR34","first-page":"137","volume":"3","author":"A Sarkar","year":"2012","unstructured":"Sarkar A, Mandal J (2012) Secured wireless communication using fuzzy logic based high speed public-key cryptography (FLHSPKC). Int J Adv Comput Sci Appl (IJACSA) 3(10):137\u2013145","journal-title":"Int J Adv Comput Sci Appl (IJACSA)"},{"issue":"7","key":"344_CR35","first-page":"267","volume":"3","author":"A Sarkar","year":"2012","unstructured":"Sarkar A, Mandal J (2012) Swarm intelligence based faster public-key cryptography in wireless communication (SIFPKC). Int J Comput Sci Eng Technol (IJCSET) 3(7):267\u2013273","journal-title":"Int J Comput Sci Eng Technol (IJCSET)"},{"key":"344_CR36","doi-asserted-by":"publisher","first-page":"16435","DOI":"10.1109\/ACCESS.2021.3052884","volume":"9","author":"A Sarkar","year":"2021","unstructured":"Sarkar A, Khan MZ, Singh MM, Noorwali A, Chakraborty C, Pani SK (2021) Artificial neural synchronization using nature inspired whale optimization. IEEE Access 9:16435\u201316447. https:\/\/doi.org\/10.1109\/ACCESS.2021.3052884","journal-title":"IEEE Access"},{"key":"344_CR37","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.69.066137","author":"LN Shacham","year":"2004","unstructured":"Shacham LN, Klein E, Mislovaty R, Kanter I, Kinzel W (2004) Cooperating attackers in neural cryptography. Phys Rev E. https:\/\/doi.org\/10.1103\/physreve.69.066137","journal-title":"Phys Rev E"},{"issue":"2","key":"344_CR38","doi-asserted-by":"publisher","first-page":"371","DOI":"10.5013\/ijssst.a.21.02.37","volume":"21","author":"E Shishniashvili","year":"2020","unstructured":"Shishniashvili E, Mamisashvili L, Mirtskhulava L (2020) Enhancing IoT security using multi-layer feedforward neural network with tree parity machine elements. Int J Simul Syst Sci Technol 21(2):371\u2013383. https:\/\/doi.org\/10.5013\/ijssst.a.21.02.37","journal-title":"Int J Simul Syst Sci Technol"},{"key":"344_CR39","doi-asserted-by":"crossref","unstructured":"Z\u00e7akmak BO, ilen AOZ, lu UY, in KC (2019) Neural and quantum cryptography in big data: a review. In: Proceedings of the 2019 IEEE International Conference on big data, pp 2413\u20132417","DOI":"10.1109\/BigData47090.2019.9006238"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-021-00344-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-021-00344-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-021-00344-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T12:33:01Z","timestamp":1646310781000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-021-00344-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,29]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["344"],"URL":"https:\/\/doi.org\/10.1007\/s40747-021-00344-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2021,4,29]]},"assertion":[{"value":"1 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}