A Dynamic Scalable Security Model for Data at Rest in fog Computing Environment

Fog computing brings cloud services closer to the network’s Edge. Despite various applications in today’s world, these applications lack in data security aspects. Developers have few solutions that need to be tested thoroughly. Data encipherment is one of the most popular mechanisms to protect data confidentiality, data integrity, etc. We propose two steps flexible dynamic scalable model in which the system will dynamically choose an encryption mechanism depending on the access frequency of data being encrypted. If data is frequently accessed, then the model will choose the algorithm with minimum computational complexity. In next step, model will use a scalable approach to decide the security strength needed by determining size of encryption key. A longer key will be used to encrypt more sensitive and secretive data automatically by security model and a smaller key will be used to encrypt public data or less sensitive, saving the fog node from computation overload. Our model is more secure and dynamic in nature with scalable security strength.

Download Full-text

Controlled Intelligent Agents' Security Model for Multi-Tenant Cloud Computing Infrastructures

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2017010101 ◽

2017 ◽

Vol 9 (1) ◽

pp. 1-13

Author(s):

Howard Hamilton ◽

Hadi Alasti

Keyword(s):

Cloud Computing ◽

Data Security ◽

Intelligent Agents ◽

Cloud Service ◽

Cloud Services ◽

Service Users ◽

Security Model ◽

Computing Environment ◽

Cloud Computing Environment ◽

Service Models

Data security in the cloud continues to be a huge concern. The adoption of cloud services continues to increase with more businesses transitioning from on premise technology infrastructures to outsourcing cloud-based infrastructures. As the cloud becomes more popular, users are increasingly demanding control over critical security elements of the data and technology assets that are in the cloud. In addition, there are still cries for greater data and security in the cloud. The goal of this paper is to provide cloud service users with greater control over data security in the cloud while at the same time optimizing overall security in the multi-tenant cloud computing environment. This paper introduces cloud-based intelligent agents that are configurable by the users and are expected to give greater compliance for data security in any of the cloud service models.

Download Full-text

Controlled Intelligent Agents' Security Model for Multi-Tenant Cloud Computing Infrastructures

Cyber Security and Threats ◽

10.4018/978-1-5225-5634-3.ch025 ◽

2018 ◽

pp. 471-484

Author(s):

Howard Hamilton ◽

Hadi Alasti

Keyword(s):

Cloud Computing ◽

Data Security ◽

Intelligent Agents ◽

Cloud Service ◽

Cloud Services ◽

Service Users ◽

Security Model ◽

Computing Environment ◽

Cloud Computing Environment ◽

Service Models

Download Full-text

Enhanced Data Security for Public Cloud Environment with Secured Hybrid Encryption Authentication Mechanisms

Scalable Computing Practice and Experience ◽

10.12694/scpe.v19i4.1422 ◽

2018 ◽

Vol 19 (4) ◽

pp. 351-360

Author(s):

Prabu S ◽

Gpinath Ganapathy ◽

Ranjan Goyal

Keyword(s):

Cloud Computing ◽

Data Storage ◽

Data Security ◽

Cloud Storage ◽

Cloud Service ◽

Security Model ◽

Public Cloud ◽

Computing Environment ◽

Data Outsourcing ◽

Cloud Computing Environment

Cloud computing is an evolving computing technology that provides many services such as software and storage. With the introduction of cloud storage, the security of outsourced data has become a major issue in cloud computing. Data storage in cloud computing environment needs to be secured in order to provide a safe and foolproof security for data outsourcing of the cloud service users. This paper presents a model for security of data in public cloud storage environment which successfully detects the unauthenticated access or any anomaly in the data. The proposed authentication model along with the data security model presented in this paper shows that this model is the best model suitable for securing the data in cloud computing environment

Download Full-text

Classification of File Data Based on Confidentiality in Cloud Computing using K-NN Classifier

International Journal of Business Analytics ◽

10.4018/ijban.2016040104 ◽

2016 ◽

Vol 3 (2) ◽

pp. 61-78 ◽

Cited By ~ 2

Author(s):

Munwar Ali Zardari ◽

Low Tang Jung

Keyword(s):

Cloud Computing ◽

Data Storage ◽

Virtual Machine ◽

Data Security ◽

Virtual Machines ◽

Data Classification ◽

Data Encryption ◽

Cloud Services ◽

Security Level ◽

Public Data

Cloud computing is a new paradigm model that offers different services to its customers. The increasing number of users for cloud services i.e. software, platform or infrastructure is one of the major reasons for security threats for customers' data. Some major security issues are highlighted in data storage service in the literature. Data of thousands of users are stored on a single centralized place where the possibility of data threat is high. There are many techniques discussed in the literature to keep data secure in the cloud, such as data encryption, private cloud and multiple clouds concepts. Data encryption is used to encrypt the data or change the format of the data into the unreadable format that unauthorized users could not understand even if they succeed to get access of the data. Data encryption is very expensive technique, it takes time to encrypt and decrypt the data. Deciding the security approach for data security without understanding the security needs of the data is a technically not a valid approach. It is a basic requirement that one should understand the security level of data before applying data encryption security approach. To discover the data security level of the data, the authors used machine learning approach in the cloud. In this paper, a data classification approach is proposed for the cloud and is implemented in a virtual machine named as Master Virtual Machine (Vmm). Other Vms are the slave virtual machines which will receive from Vmm the classified information for further processing in cloud. In this study the authors used three (3) virtual machines, one master Vmm and two slaves Vms. The master Vmm is responsible for finding the classes of the data based on its confidentiality level. The data is classified into two classes, confidential (sensitive) and non-confidential (non-sensitive/public) data using K-NN algorithm. After classification phase, the security phase (encryption phase) shall encrypt only the confidential (sensitive) data. The confidentiality based data classification is using K-NN in cloud virtual environment as the method to encrypt efficiently the only confidential data. The proposed approach is efficient and memory space friendly and these are the major findings of this work.

Download Full-text

Cloud Data Security Model Using Modified Decoy Technique In Fog Computing For E-Healthcare

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1065/1/012044 ◽

2021 ◽

Vol 1065 (1) ◽

pp. 012044

Author(s):

Dr. P. Maragathavalli ◽

S. Atchaya ◽

N. Kaliyaperumal ◽

S. Saranya

Keyword(s):

Data Security ◽

Fog Computing ◽

Security Model ◽

Cloud Data

Download Full-text

Data Security Model in Cloud Computing Environment

Advances in Intelligent Systems and Computing - Cyber Security ◽

10.1007/978-981-10-8536-9_10 ◽

2018 ◽

pp. 91-100

Author(s):

Meena Kumari ◽

Rajender Nath

Keyword(s):

Cloud Computing ◽

Data Security ◽

Security Model ◽

Computing Environment ◽

Cloud Computing Environment

Download Full-text

OB-MECC: An Efficient Confidentiality and Security Enhancement for Cloud Storage System

Journal of Cyber Security and Mobility ◽

10.13052/jcsm2245-1439.944 ◽

2021 ◽

Author(s):

P. NagaRaju ◽

N. Nagamalleswara Rao

Keyword(s):

Cloud Computing ◽

Data Security ◽

Cloud Storage ◽

Storage System ◽

Cloud Services ◽

Security Level ◽

Data Confidentiality ◽

Security Enhancement ◽

Reducing Costs ◽

Main Barrier

Cloud computing (CC) is one amongst the developing technologies, which get more attention from academia as well as industries. It offers diverse benefits like sharing computing resources, service flexibility, reducing costs, etc. The Cloud Services Provider (CSP) is accountable for the data that are delivered to the cloud. The threat of seeing the stored data and using sensitive raw data by strangers is the main barrier in the utilization of cloud services. So, Data Security (DS) along with privacy is the chief issue, which is an obstacle while adopting the CC. Countless techniques are existent for ensuring data confidentiality, but they do not completely give protection to the data. To trounce these drawbacks, this paper introduces the Obfuscation (OB) based Modified Elliptical Curve Cryptography (MECC) algorithm for protecting data as of malicious attacks, which is termed as OB-MECC. Primarily, the proposed method obfuscates the data before they are uploaded to the cloud. For the OB of the data, the proposed work employs methods like substitution cipher (SC), position update, Ceaser cipher, binary conversion, 8-bit binary conversion, decimal(), two complex(), and ASCII(). Then, encryption of the obfuscated data is done with the utilization of the MECC algorithm. After encryption, the data on the cloud is retrieved. The retrieved data is then decrypted by reversing the OB and encryption process to get the actual data. The outcomes corroborate that the confidentiality and security level are maximum for the proposed OB-MECC when contrasted to the existing approaches.

Download Full-text