Big Data Handling Approach for Unauthorized Cloud Computing Access

Razaque, Abdul; Shaldanbayeva, Nazerke; Alotaibi, Bandar; Alotaibi, Munif; Murat, Akhmetov; Alotaibi, Aziz

doi:10.3390/electronics11010137

Cited by 14 publications

(7 citation statements)

References 44 publications

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“…However, it failed to utilize an elliptic curve-based cryptographic technique to reduce the network utilization in the cloud environment. Razaque, A., et al [4] developed a Hybrid Unauthorized Data Handling (HUDH) for unauthorized Cloud Computing Access, and effectively reduced the possibility of unauthorized data access under less computational cost in cloud computing, but it did not consider various quality of service parameters to increase the confidentiality of user access. Singamaneni, K.K., et al [5] devised a Quantum Hash-Centric Cipher Policy-Attribute-Based Encipherment (QH-CPABE) for Cloud Data Integrity and Confidentiality.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, this method was not suitable for multimodal data analysis to provide security for cloud-based services. • The HUDH scheme used in [4] effectively enhanced the efficiency of encrypted data storage in the cloud computing environment. However, it failed to significantly calculate the ciphertext by combining it with the Spark computing framework.…”

Section: Challengesmentioning

confidence: 99%

“…The comparative schemes regarded for validating the effectiveness of devised IABHE are the ABAC approach [1], the EStore model [2], the TDES approach [3], and HUDH [4].…”

Section: Comparative Techniquesmentioning

confidence: 99%

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IABHE: Identity and Attribute-based Honey Encryption Algorithm and Deep Learning-based Key Generation for Securing Big Data in Hadoop Framework

Siyal,

Long

2024

Preprint

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Nowadays, Hadoop is considered promising to store and reliably process big data in the cloud computing platform, and it also provides low-cost and flexible services for large amounts of data. However, the possibility of malicious attacks on stored or processed data has increased due to the absence of inherent security mechanisms in Hadoop. In this approach, an Identity and Attribute-Based Honey Encryption Algorithm (IABHE) is developed for the encryption of data to offer security to the big data in the Hadoop framework. Here, the system model of the cloud is considered initially. Then, acquired input big data is applied to the MapReduce framework, where data encryption is performed by IABHE, which incorporates identity and attribute-based encryption with honey encryption. Moreover, a deep learning model, PyramidNet is utilized for the generation of secret keys used in encryption. Moreover, the encrypted data obtained in the mapper phase is aggregated by performing polynomial interpolation in the reducer phase. Finally, encrypted data is securely saved in the cloud. Here, the efficiency of IABHE is evaluated utilizing various performance metrics and it attained better values of encryption time at 17.756sec, decryption time at 8.767sec, and key complexity at 0.368.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

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IABHE: Identity and Attribute-based Honey Encryption Algorithm and Deep Learning-based Key Generation for Securing Big Data in Hadoop Framework

Siyal,

Long

2024

Preprint

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“…For that purpose, the proposed framework was capable of providing a lightweight security solution with a minimum key size. A hybrid scheme combining data encryption, access control, and intrusion detection was proposed by Razaque et al for big data security in the cloud environment [31]. However, due to its hybrid nature, the procedure may be computationally expensive.…”

Section: Related Workmentioning

confidence: 99%

An Improved End-to-End Data Security Approach for Cloud Computing

Ghosh¹,

Verma²,

Ghosh³

2023

Preprint

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<p>Cloud computing is one of the major cutting-edge technology which is in a gigantic growth to redefine the aspect of computation with its service-oriented computing. It has addressed the issue of owning and managing the computational infrastructure by provid- ing the service through a pay-and-use model as an ultimate consequence of saving money and time. However, the major hindrance can be security breaches, particularly when the sender uploads or receiver downloads the data from a remotely accessed server. A very generic approach to ensuring data security through different encryption techniques, but it might not be able to maintain the security standard. This paper proposed an end-to-end data security approach from sender side to receiver side by adding extra padding sequences, randomized salting followed by hashing and the encryption technique. The efficiency of the proposed approach is analyzed through both the simulated system and mathematical formulations with several performance metrics, like cipher text size, encryption and decryption time, encryption and decryption throughput. Further, the performances are compared with contem- porary algorithms and results have shown that the proposed algorithm creates a larger cipher text which is almost impossible to crack due to randomization modules. Though it has a significantly larger encryption and decryption time, as a consequence, lower throughput also. However, here our prime concern is ensuring security, not the time required to encrypt the text. </p>

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“…It requires the specification of all important events and conditions that should be taken into account and strong procedures of implementing options for combining; • collection of the data for estimation and prediction of CIS models' parameters. It concerns, first of all, information about vulnerabilities and cyber-attacks, and application of ML to calculate parameters; • the C5 approach should be added by proactive techniques for assessment and assurance of CIS dependability, cybersecurity, privacy, and resilience based on Big Data analytics [59,60] and machine learning [61] methods to analyze data and support decision-making about choice and combining and recombining models; • application of this approach for combining hidden MMs and SMMs to assess privacy [62] extending the model base. Institutional Review Board Statement: Not applicable.…”

mentioning

confidence: 99%

Combining Markov and Semi-Markov Modelling for Assessing Availability and Cybersecurity of Cloud and IoT Systems

et al. 2022

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This paper suggests a strategy (C5) for assessing cloud and IoT system (CIS) dependability, availability, and cybersecurity based on the continuous collection, comparison, choice, and combination of Markov and semi-Markov models (MMs and SMMs). It proposes the systematic building of an adequate and accurate model to evaluate CISs considering (1) continuous evolution of the model(s) together with systems induced by changes in the CIS or physical and cyber environment parameters; (2) the necessity of collecting data on faults, failures, vulnerabilities, cyber-attacks, privacy violations, and patches to obtain actual data for assessment; (3) renewing the model set based on analysis of CIS operation; (4) the possibility of choice and utilizing “off-the-shelf” models with understandable techniques for their development to assure improved accuracy of assessment; (5) renewing the models during application of CIS by time, component or mixed combining, taking into consideration different operation and maintenance events. The results obtained were algorithms for data collection and analysis, choice, and combining appropriate MM and SMMs and their different types, such as multi-fragmental and multiphase models, considering changing failure rates, cyber-attack parameters, periodical maintenance, etc. To provide and verify the approach, several private and public clouds and IoT systems were researched and discussed in the context of C5 and proposed algorithms.

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Big Data Handling Approach for Unauthorized Cloud Computing Access

Cited by 14 publications

References 44 publications

IABHE: Identity and Attribute-based Honey Encryption Algorithm and Deep Learning-based Key Generation for Securing Big Data in Hadoop Framework

IABHE: Identity and Attribute-based Honey Encryption Algorithm and Deep Learning-based Key Generation for Securing Big Data in Hadoop Framework

An Improved End-to-End Data Security Approach for Cloud Computing

Combining Markov and Semi-Markov Modelling for Assessing Availability and Cybersecurity of Cloud and IoT Systems

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