Performance Evaluation and Analysis of Advanced Symmetric Key Cryptographic Algorithms for Cloud Computing Security

Wani, Abdul Raoof; Rana, Q. P.; Pandey, Nitin

doi:10.1007/978-981-13-0589-4_24

Cited by 9 publications

(5 citation statements)

References 1 publication

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“…Wani et al 20 Compared the encryption and decryption times, memory usage, security, and performance of AES, DES, Blowfish, 3DES, RC4, IDEA, and TEA algorithms on 10 different file sizes. The study concluded that encryption time for RC4 is the shortest and that for 3DES is the longest, decryption time for RC4 and AES is the shortest and 3DES is the longest, and memory use for IDEA and TEA is lower and RC4 is higher.…”

Section: Literature Reviewmentioning

confidence: 99%

Data security techniques in cloud computing based on machine learning algorithms and cryptographic algorithms: Lightweight algorithms and genetics algorithms

Thabit

Can

Wani

et al. 2023

Concurrency and Computation

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SummaryCloud computing (CC) refers to the on‐demand availability of network resources, particularly data storage and processing power, without requiring special or direct administration by users. CC, which just made its debut as a collection of public and private data centers, provides clients with a unified platform throughout the Internet. Cloud computing has revolutionized the world, opening up new horizons with bright potential due to its performance, accessibility, low cost, and many other benefits. Due to the exponential rise of cloud computing, systems based on cloud computing now require an effective data security mechanism. Comprehensive security policies, corporate security culture, and cloud security solutions are used to ensure the level of cloud data security. Many techniques exist to protect data communication in the cloud environment, including encryption. Encryption algorithms play an important role in information security systems and various cloud computing‐based systems. Current researchers have focused on lightweight cryptography, genetics‐based cryptography, and machine learning (ML) algorithms for security in CC. This review study analyses CC security threats, problems, and solutions that use one or more algorithms. The work discusses several lightweight cryptographies, genetics‐based cryptography and different ML algorithms that are used to overcome cloud security issues, including supervised, unsupervised, semi‐supervised, and reinforcement learning. Moreover, we enlist future research directions to secure CC models.

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Section: Literature Reviewmentioning

confidence: 99%

Data security techniques in cloud computing based on machine learning algorithms and cryptographic algorithms: Lightweight algorithms and genetics algorithms

Thabit

Can

Wani

et al. 2023

Concurrency and Computation

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“…In Ref. [21], the performance of some symmetric cryptographic algorithms has been studied in terms of runtime parameters and memory consumption.…”

Section: Related Workmentioning

confidence: 99%

Security-Level Improvement of IoT-Based Systems Using Biometric Features

Moradi

Moradkhani

Tavakoli

2022

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is reported as a main research topic in the current decade. It will be possible to connect smart devices to each other using IoT, a platform such as the Internet. However, the expansion and intrusion of such a large network raises some new security issues and risks related to the disclosure of user confidential information where these devices are subject to hacker threats and intrusions. Traditional security systems were password based. In this paper, after reviewing the actions taken in this regard, the improvement level of biometric security compared with traditional password-based methods will be proven in section three using the Markov model. By considering the results of the evaluation, the probability of occurrence of security problems is decreased by 90.71% by applying biometric features. Then, multi-layer security architecture with biometric features and coding systems is suggested to increase security. In the first layer, the fingerprint recognition algorithm is dependent on the module, and the U.are.U 5100 module provides more security than others. In the second layer, the Hash mechanism of the MD5 algorithm is, on average, 63.21% more efficient. By determining the properties of the first two architectural layers and ultimately for the IoT application layer, empirical methods and hardware platforms for the Internet of things are used. Concerning the simulation results, the suggested mechanism enhances the system security by 120.38% on average, which is 106.23, 110.45, and 144.46% of relative improvement compared with IoT sensors, controller layer mechanisms, and application layer mechanisms, respectively.

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“…To demonstrate the efficacy of the proposed approach, it was compared to seven other methods: DES-based data protection [Elgeldawi, et al (2019)], TDES based data security [Elgeldawi, et al (2019)], AES-based data security [Wani, et al (2019)],…”

Section: Comparative Analysismentioning

confidence: 99%

“…Blow Fish-based data security [Wani, et al (2019)], ECC-based data security [Dheepak T (2021)], RBAC-based data security [Jayasankar T, et al (2021)] and Two Fish based data security [Jayasankar T, et al (2021)]. Figure 25 shows the comparison results analyzed in terms of the retrieval time.…”

Section: Comparative Analysismentioning

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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Performance Evaluation and Analysis of Advanced Symmetric Key Cryptographic Algorithms for Cloud Computing Security

Cited by 9 publications

References 1 publication

Data security techniques in cloud computing based on machine learning algorithms and cryptographic algorithms: Lightweight algorithms and genetics algorithms

Data security techniques in cloud computing based on machine learning algorithms and cryptographic algorithms: Lightweight algorithms and genetics algorithms

Security-Level Improvement of IoT-Based Systems Using Biometric Features

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

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