Enhancing Cloud Data Security using Artificial Neural Networks for Users’ Account Hijacking Security Threats DOI Open Access
Sumeet Gill,

Renu Devi

Indian Journal of Science and Technology, Journal Year: 2024, Volume and Issue: 17(34), P. 3538 - 3552

Published: Sept. 2, 2024

Objectives: To ensure the security of passwords cloud users' accounts that cannot be decrypted easily by any software or hackers. Methods: In this manuscript, we have designed an experimental setup using a feed-forward back-propagation algorithm Artificial Neural Networks techniques to data security. For purpose, utilized password-based datasets created us. 70% are allocated for training and 30% testing validation purposes. training, TRAINLM function, LEARNGDM adaptive performance function is MSE, PURELIN TANSIG transfer neurons from input hidden layer output has been used. The minimum learning rate set 0.001. Findings: A username password required access services. cloud-based storage system used store login credentials. Due inadequate security, attackers use hacking gain users’ accounts. Attackers steal data, resources, services these accounts, neural network techniques. Through approach, stored in weight matrix, which multi-dimensional structure. Novelty: dimensions matrix obscured, making it impossible hackers determine its As result, conclude effectively secured on server. Keywords: Networks, Cloud Computing, Data Security, Feed-Forward Back-Propagation Algorithm, Machine Learning

Language: Английский

CAD Design for CNC Equipments and Industrial Manufacturing Using CNN DOI

Ashmeet Kaur

Published: July 28, 2023

Language: Английский

Citations

1

Enhancing Cloud Data Security using Artificial Neural Networks for Users’ Account Hijacking Security Threats DOI Open Access
Sumeet Gill,

Renu Devi

Indian Journal of Science and Technology, Journal Year: 2024, Volume and Issue: 17(34), P. 3538 - 3552

Published: Sept. 2, 2024

Objectives: To ensure the security of passwords cloud users' accounts that cannot be decrypted easily by any software or hackers. Methods: In this manuscript, we have designed an experimental setup using a feed-forward back-propagation algorithm Artificial Neural Networks techniques to data security. For purpose, utilized password-based datasets created us. 70% are allocated for training and 30% testing validation purposes. training, TRAINLM function, LEARNGDM adaptive performance function is MSE, PURELIN TANSIG transfer neurons from input hidden layer output has been used. The minimum learning rate set 0.001. Findings: A username password required access services. cloud-based storage system used store login credentials. Due inadequate security, attackers use hacking gain users’ accounts. Attackers steal data, resources, services these accounts, neural network techniques. Through approach, stored in weight matrix, which multi-dimensional structure. Novelty: dimensions matrix obscured, making it impossible hackers determine its As result, conclude effectively secured on server. Keywords: Networks, Cloud Computing, Data Security, Feed-Forward Back-Propagation Algorithm, Machine Learning

Language: Английский

Citations

0