Security by Design for Big Data Frameworks Over Cloud Computing

Awaysheh, Feras M.; Aladwan, Mohammad N.; Alazab, Mamoun; Alawadi, Sadi; Dominguez, Jessica A.; Pena, Tomás F.

doi:10.1109/tem.2020.3045661

Cited by 81 publications

(29 citation statements)

References 45 publications

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“…This data needs to be secured in the cloud platform for organisations using a cloud system, although it remains vulnerable to attacks. Depending on the level of security at each management and control level, this data can be secured or exposed to the public [ 18 ].…”

Section: Abstraction Of Data Levelmentioning

confidence: 99%

Data-Driven Threat Analysis for Ensuring Security in Cloud Enabled Systems

Alwaheidi

Islam

2022

Sensors

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Cloud computing offers many benefits including business flexibility, scalability and cost savings but despite these benefits, there exist threats that require adequate attention for secure service delivery. Threats in a cloud-based system need to be considered from a holistic perspective that accounts for data, application, infrastructure and service, which can pose potential risks. Data certainly plays a critical role within the whole ecosystem and organisations should take account of and protect data from any potential threats. Due to the variation of data types, status, and location, understanding the potential security concerns in cloud-based infrastructures is more complex than in a traditional system. The existing threat modeling approaches lack the ability to analyse and prioritise data-related threats. The main contribution of the paper is a novel data-driven threat analysis (d-TM) approach for the cloud-based systems. The main motivation of d-TM is the integration of data from three levels of abstractions, i.e., management, control, and business and three phases, i.e., storage, process and transmittance, within each level. The d-TM provides a systematic flow of attack surface analysis from the user agent to the cloud service provider based on the threat layers in cloud computing. Finally, a cloud-based use case scenario was used to demonstrate the applicability of the proposed approach. The result shows that d-TM revealed four critical threats out of the seven threats based on the identified assets. The threats targeted management and business data in general, while targeting data in process and transit more specifically.

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Section: Abstraction Of Data Levelmentioning

confidence: 99%

Data-Driven Threat Analysis for Ensuring Security in Cloud Enabled Systems

Alwaheidi

Islam

2022

Sensors

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“…The access control strategy was implemented using searchable symmetric encryption. Awaysheh, F.M., Aladwan, M.N., et al [22] have investigated the critical role of cloud designs in the broad adoption of cloud architectures in 2021. For the implementation of BD frameworks over cloud computing, they utilized a unique security-by-design framework.…”

Section: Literature Surveymentioning

confidence: 99%

An Effective Meta Heuristic Based Dynamic Fine Grained Data Security Framework for Big Data

Gupta

Samad

Garg

et al. 2022

Preprint

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Medical records are transmitted between medical institutions using cloud-based Electronic Health Record (EHR) systems, which are intended to improve various medical services. Due to the potential of data breaches and the resultant loss of patient data, medical organizations find it challenging to employ cloud-based electronic medical record systems. EHR systems frequently necessitate high transmission costs, energy use, and time loss for physicians and patients. Furthermore, EHR security is a critical concern that jeopardizes patient privacy. Compared to a single system, cloud-based EHR solutions may bring extra security concerns as the system architecture gets more intricate. Access control strategies and the development of efficient security mechanisms for cloud-based EHR data are critical. For privacy reasons, the Dynamic Constrained Message Authentication (DCMA) technique is used in the proposed system to encrypt the outsource medical data by using symmetric key cryptography which uses the Seagull Optimization Algorithm (SOA) for choosing the best random keys for encryption and then resultant data is hashed using the SHA-256 technique. The system is developed in Python language, and the results are assessed using performance metrics including delay time, security rate, false error rate (FER), storage time, retrieval time, throughput ratio, encryption and decryption time, accuracy rate, key generation time, and security. The implemented system is superior in terms of security because it adopts the advance random secret keys generation which adds more security to the system of about 94% with less delay and loss ratio.

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“…is framework permits the mapping of big cloud security domain information to the exceptional practices in the plan Step [24].…”

Section: Related Workmentioning

confidence: 99%

“…It addressed the big cloud-specific protection demands to absolutely illustrate the number protection elements in a conceptual context. This framework permits the mapping of big cloud security domain information to the exceptional practices in the plan Step [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

A New Secure Model for Data Protection over Cloud Computing

Sauber

Elkafrawy

Shawish

et al. 2021

Computational Intelligence and Neuroscience

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The main goal of any data storage model on the cloud is accessing data in an easy way without risking its security. A security consideration is a major aspect in any cloud data storage model to provide safety and efficiency. In this paper, we propose a secure data protection model over the cloud. The proposed model presents a solution to some security issues of cloud such as data protection from any violations and protection from a fake authorized identity user, which adversely affects the security of the cloud. This paper includes multiple issues and challenges with cloud computing that impairs security and privacy of data. It presents the threats and attacks that affect data residing in the cloud. Our proposed model provides the benefits and effectiveness of security in cloud computing such as enhancement of the encryption of data in the cloud. It provides security and scalability of data sharing for users on the cloud computing. Our model achieves the security functions over cloud computing such as identification and authentication, authorization, and encryption. Also, this model protects the system from any fake data owner who enters malicious information that may destroy the main goal of cloud services. We develop the one-time password (OTP) as a logging technique and uploading technique to protect users and data owners from any fake unauthorized access to the cloud. We implement our model using a simulation of the model called Next Generation Secure Cloud Server (NG-Cloud). These results increase the security protection techniques for end user and data owner from fake user and fake data owner in the cloud.

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Security by Design for Big Data Frameworks Over Cloud Computing

Cited by 81 publications

References 45 publications

Data-Driven Threat Analysis for Ensuring Security in Cloud Enabled Systems

Data-Driven Threat Analysis for Ensuring Security in Cloud Enabled Systems

An Effective Meta Heuristic Based Dynamic Fine Grained Data Security Framework for Big Data

A New Secure Model for Data Protection over Cloud Computing

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