An Efficient Optimization and Secured Triple Data Encryption Standard Using Enhanced Key Scheduling Algorithm

Vuppala, Akshitha; Roshan, Rishu; Nawaz, Shaik Sarfaraz; Ravindra, J. V. R.

doi:10.1016/j.procs.2020.04.113

Cited by 32 publications

(9 citation statements)

References 10 publications

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“…To prevent these attacks, the DES cipher was reconfirmed as the triple data encryption standard (3DES), using the DES cipher three times, i.e., enciphering -decryption-enciphering to encipher the 64-bit plain text into a 64-bit cipher text. This can be done using a 192-bit key composed of Key 1, Key 2, and Key 3 of 64-bit each for enciphering, deciphering, and enciphering, respectively, as depicted in Figure 3 [11]. [11].…”

Section: 23triple Data Encryption Standard (Triple-des)mentioning

confidence: 99%

Block Ciphers Analysis Based on a Fully Connected Neural Network

Alwan

Kashmar

2023

IHJPAS

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With the development of high-speed network technologies, there has been a recent rise in the transfer of significant amounts of sensitive data across the Internet and other open channels. The data will be encrypted using the same key for both Triple Data Encryption Standard (TDES) and Advanced Encryption Standard (AES), with block cipher modes called cipher Block Chaining (CBC) and Electronic CodeBook (ECB). Block ciphers are often used for secure data storage in fixed hard drives, portable devices, and safe network data transport. Therefore, to assess the security of the encryption method, it is necessary to become familiar with and evaluate the algorithms of cryptographic systems. Block cipher users need to be sure that the ciphers they employ are secure against various attacks. A Fully Connected Neural Network (FCNN) model was initially used to assess how well the models were classified. Then, all models, including encoder models, were assessed using True Positive (TP) measures for successful classification of the discovered encoder and False Positive (FP) measures for imprecise categorization. The accuracy value, retrieval, loss, precision value, and F1 score were then calculated using a confusion matrix to assess the model's efficacy (abbreviated as F1). ECB results with an accuracy of 85% and CBC results with an accuracy of 88% were produced, and the parameters of the FCNN model were tweaked to provide better results. These results helped to identify the encryption algorithm more precisely and evaluate it.

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Section: 23triple Data Encryption Standard (Triple-des)mentioning

confidence: 99%

Block Ciphers Analysis Based on a Fully Connected Neural Network

Alwan

Kashmar

2023

IHJPAS

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“…Rivest-Shamir-Adleman (RSA) is used in conjunction with Advanced Encryption Standard (AES) and Triple Data Encryption Standard (TDES) to improve the security of stored data (TDES). Vuppala et al [Vuppala, et al (2020)] presented the FORTIS algorithm for sub-key generation and investigated its strength against side-channel power attacks using the Chip Whisperer R -Lite and Artix FPGA as target boards. The number of glitches that represent leakage power has been reduced by about 53.3 percent, and the power traces of the key schedule algorithm show that all of the instructions are similar, making it difficult to determine which operation is being performed, and the probability of guessing entropy has been reduced in 86.6 percent of cases.…”

Section: Triple Data Encryption Standardmentioning

confidence: 99%

An Analytical approach for optimal secured data storage on cloud server for online education platform

Pandian¹,

Columbus²

2022

Preprint

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Abstract. Cloud computing is becoming increasingly popular in the IT business because of its higher performance, widespread access, cheap cost, and other benefits. It is also a pay-as-you-go approach; hence, anyone can access cloud data from anywhere, and it is employed in education platforms for online classes due to its ease of use. However, many educational institutions hesitate to use the cloud educational platform due to security and privacy issues. Hence in this study, the performance analysis of various cryptographic algorithms such as Elliptic Curve Cryptography (ECC), Advanced encryption standard (AES), Two Fish, Blowfish, Data Encryption Standard (DES), Triple Data Encryption Standard (TDES) and role-based access control (RBAC) was analyzed and compared with each other in a view to ensuring the protection of cloud data storage used for educational purpose in the NPTEL database. Encryption time, decryption time, and retrieval time with different data sizes were used as performance evaluation factors to figure out the best End-To-End Encryption security in a network system. Moreover, an ElGamal SBO with Delta Competitive NN Cryptography has been proposed in which ElGamal Stag beetle optimization performs ElGamal encryption with the generation of an optimized key with low execution time, thereby allowing only authorized users to access the educational data in the cloud and the data transmission has been secured using Delta Competitive NN that minimizes vulnerable attacks while controlling the decryption activity. Results showed that the proposed ElGamal SBO with Delta Competitive NN Cryptography performs better than all other techniques in terms of retrieval time, encryption time, communication cost, computational overhead and decryption time and hence when applied in a security scenario, it can improve the encryption effectiveness.

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“…Generative adversarial network can effectively distinguish the distribution of high-dimensional and complex data, and it applies adversarial method to mine the generative algorithm of data distribution [11]. In generative adversarial network, there are two main structures: generative module S and discriminant module P .…”

Section: Adversarial Neural Networkmentioning

confidence: 99%

Research on trade data encryption of tobacco enterprises based on adversarial neural network

Zhang

2021

Soft Comput

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In order to overcome the problems of long encryption process, low data security and poor anti attack rate of traditional tobacco enterprise trade data encryption methods, a trade data encryption of tobacco enterprises based on adversarial neural network is proposed. This method optimizes the traditional neural network by generating countermeasure network, so as to form adversarial neural network. In the adversarial neural network, the encryption processing of tobacco enterprise trade data is completed through data feature classification, design of tobacco enterprise trade data encryption protocol and data encryption channel. The experimental results show that the encryption process of this method takes between 6s-20s, and the encryption efficiency is high, and this method can effectively scramble the original arrangement of the data, so as to effectively hide the effective information in the tobacco enterprise trade data, improve the anti attack rate of the data, and effectively improve the security of the data.

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An Efficient Optimization and Secured Triple Data Encryption Standard Using Enhanced Key Scheduling Algorithm

Cited by 32 publications

References 10 publications

Block Ciphers Analysis Based on a Fully Connected Neural Network

Block Ciphers Analysis Based on a Fully Connected Neural Network

An Analytical approach for optimal secured data storage on cloud server for online education platform

Research on trade data encryption of tobacco enterprises based on adversarial neural network

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