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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/476636
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dc.contributor.advisorRavie Chandren Muniyandi, Assoc. Prof. Dr.
dc.contributor.authorOthman Araf Hanshal (P74146)
dc.date.accessioned2023-10-06T09:22:57Z-
dc.date.available2023-10-06T09:22:57Z-
dc.date.issued2016-08-10
dc.identifier.otherukmvital:122217
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/476636-
dc.descriptionAchieving secure communication across insecure networks and also securing secret and sensitive data from unauthorized access over public networks is a big issue in cloud servers. For data security and also excellent key strength, it requires that the keys used for encryption be unique and random in nature. A key generated by any process which is also random in nature cannot be derived from a public key, as it could allow the man-in-the-middle to easily attack the network and gain access to sensitive data. Hence a system is required whereby the keys used to encrypt and decrypt are unique and are mixed (encrypted) to generate some intermediate keys that are again random which secure both data and keys. In this research, we investigate about how much time is needed to encrypt and decrypt for ECDH between cloud user and cloud server - which are simulated as GUI tools. The finding is that the time consumption will be increased when the text file numbers are increasing as well. Thus, our experimental results show better improvement in terms of time when applied to an artificial neural network for ECDH key exchanges and we will see the statistical significance on sequence first, the time of encryption ECDH 6.758 %. & ECDHNN 5.05% & ECDHNNGA 3.44% and the time of decryption, also show ECDH 6.64 % & ECDHNN 5.15 % and ECDHNNGA 3.17% Moreover, when this research has developed the ECDHANN over genetic algorithm the result and statistical significance shows the best outcome of this research challenge was not for time only, but also for the performance ECDHNN 0.47 % & ECDHNNGA 0.24 % and high impact error results ECDHNN -0.22 % ECDHNNGA -0.32594 % and for error decryption ECDHNN -0.51 % and ECDHNNGA -0.22 % .,Master of Computer Science,Certification of Master's / Doctoral Thesis" is not available"
dc.language.isoeng
dc.publisherUKM, Bangi
dc.relationFaculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat
dc.rightsUKM
dc.subjectArtificial intelligence
dc.subjectCloud computing
dc.subjectUniversiti Kebangsaan Malaysia -- Dissertations
dc.subjectDissertations, Academic -- Malaysia
dc.titleElliptic curve Diffie-Hellman random keys using artificial neural network and genetic algorithm for secure data over private cloud
dc.typetheses
dc.format.pages77
dc.identifier.callnoTA347.A78H336 2016 3 tesis
dc.identifier.barcode005571(2021)(PL2)
Appears in Collections:Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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