Effective Cryptographic Renewal Method with Rivest-Shamir-Adleman, Modified Artificial Bee Colony Optimization to Achieve Secure Cloud Storage and Computation in Cloud Computing

2020 ◽  
Vol 17 (6) ◽  
pp. 2699-2705
Author(s):  
Mayank Aggarwal ◽  
Anju Malik ◽  
Mani Madhukar
Keyword(s):  
Cloud Computing ◽  
Data Encryption ◽  
Security And Privacy ◽  
Online Information ◽  
Secret Key ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Encryption And Decryption ◽  
User Data ◽  
Bee Colony Optimization

With the tremendous development of the online information, the term “Cloud Computing” offers multiple kinds of outsourcing and storage services to the user. Data security and privacy issues introduces multiple hurdles into it such as data leakage, calculating the key for data encryption, lack of proper security policies and renewal of application in cloud environment. One of the main issues is how to store encrypted data, decrypt it when required and renewal of application to face the security challenges. In this work we have proposed a secured and timestamp value based approach using RSA-MABC algorithm for encryption and decryption process. The work has been simulated in Cloudsim and results show our approach takes less time for encryption and decryption with added security features of renewing the secret key based on timestamp. Our results of encryption and decryption time along with the results of ttest shows that our work is better than exiting approaches.

scholarly journals Improved cloud data transfer security using hybrid encryption algorithm

2020 ◽  
Vol 20 (1) ◽  
pp. 521
Author(s):  
Samar Zaineldeen ◽  
Abdelrahim Ate
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Data Encryption ◽  
Remote Access ◽  
Security And Privacy ◽  
Encryption Algorithm ◽  
Memory Consumption ◽  
Hybrid Encryption ◽  
Encryption And Decryption ◽  
Encryption Algorithms

Cloud computing is a model of sophisticated computing which has a strong effect on data innovation. Cloud computing offers remote access to shared computerized assets in the stored cloud. Operationally cloud servers utilize Web services that give enormus advantage to the user in a variety of applications such as banking and finance, storage, social networking and e-mail. Cloud computing accomplishes many of the features interrelated to elasticity, ease of utility, efficiency and performance with low cost.  There are a number of potential concerns related to security and privacy since the requirement to protect cloud computing expanded, the encryption algorithms play the key part in data and information security systems, on side these algorithms consume a considerable quantity of computing resource.This paper,presenting  a new hybrid encryption algorithm emphasising  on AES and Enhanced Homomorphic Cryptosystem (EHC) as a hybrid encryption  to guarantee the secure exchange of data between the user and the cloud server, and compression study for two proficient homomorphic encryption techniques for encoding Data Encryption Standard (DES), Advance Encryption Stander (AES).With The proposed techniques an evaluation has been conducted for those encryption algorithms at diverse file sizes of data, to evaluate time taking for encryption and decryption, throughput, memory consumption and power consumption. The major finding was thats the proposed method has the extremity throughput; memory consumption and our proposed work took advantage of the least time taken in sec for encryption and decryption.

Mining of structural motifs in proteins using artificial bee colony optimization framework for druggability

2021 ◽  
Author(s):  
L. S. Suma ◽  
S. S. Vinod Chandra
Keyword(s):  
Artificial Bee Colony ◽  
Area Under The Curve ◽  
Docking Studies ◽  
Artificial Bee Colony Optimization ◽  
Optimization Framework ◽  
Bee Colony ◽  
Novel Variant ◽  
Bee Colony Optimization ◽  
Novel Strategy ◽  
Sensitivity Specificity

In this work, we have developed an optimization framework for digging out common structural patterns inherent in DNA binding proteins. A novel variant of the artificial bee colony optimization algorithm is proposed to improve the exploitation process. Experiments on four benchmark objective functions for different dimensions proved the speedier convergence of the algorithm. Also, it has generated optimum features of Helix Turn Helix structural pattern based on the objective function defined with occurrence count on secondary structure. The proposed algorithm outperformed the compared methods in convergence speed and the quality of generated motif features. The motif locations obtained using the derived common pattern are compared with the results of two other motif detection tools. 92% of tested proteins have produced matching locations with the results of the compared methods. The performance of the approach was analyzed with various measures and observed higher sensitivity, specificity and area under the curve values. A novel strategy for druggability finding by docking studies, targeting the motif locations is also discussed.

scholarly journals Pareto front feature selection based on artificial bee colony optimization

2018 ◽  
Vol 422 ◽  
pp. 462-479 ◽  
Author(s):  
Emrah Hancer ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
Dervis Karaboga ◽  
Bahriye Akay
Keyword(s):  
Feature Selection ◽  
Pareto Front ◽  
Artificial Bee Colony ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization
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