Users In Cloud Environment Research Articles

Building a Fingerprint Recognition Structure as a Security Mechanism to Verify the Identities of Users in Cloud Environment through Resnet-18

Objectives: To authenticate users employing fingerprint recognition thereby strengthening a security mechanism in a cloud environment. Methods: This study presents a novel approach for fingerprint detection in cloud systems by combining deep learning with metaheuristic optimization. A novel approach based on ResNet-18 for feature extraction and Thermal Exchange Optimization (TEO) classification is presented. This method aims to reduce data duplicity and provide safe access rights. When tested on 80% of the NIST and BDAL datasets, the proposed ResNet-18-TEO method outperforms existing models concerning recognition rates of 95.3% and 94.3% respectively. Findings: The experimental results reveal a False Acceptance Rate (FAR) spanning 0.6% to 4.8% and a False Rejection Rate (FRR) spanning 1.1% to 5.3%. Novelty: The proposed method would help cloud-based security since its fingerprint recognition time is substantially less than 25 seconds in training and 15 seconds in testing that of existing processes. In future work, this research will improvise using several deep learning techniques which are adopted using real time or research dataset. Keywords: Fingerprint recognition, Cloud, Security, Authentication, Privacy

FFEC: Fast and forward-secure equivalence-based ciphertext comparability for multiple users in cloud environment

With the expansion of cloud computing, an increasing amount of sensitive data is being encrypted and stored in public clouds to alleviate storage and management burdens. Secure equivalence-based retrieval of ciphertexts for multiple users is crucial in a cloud environment where diverse user data resides for processing purposes. Public key encryption with equality test (PKEET) has been introduced as a cryptographic tool to verify if two ciphertexts under different public keys contain the same message. However, existing PKEET schemes often face misuse of trapdoors due to their unlimited lifespan, potentially leading to unauthorized disclosure of user privacy. In this paper, we propose a novel approach called fast and forward-secure equivalence-based comparability (FFEC) for multiple users by employing a forward-secure PKEET (FS-PKEET). This restricts the retrieval process only to ciphertexts generated prior to the most recent trapdoor update. We present a concrete FS-PKEET scheme based on bilinear pairing and demonstrate its security using Bilinear Diffie–Hellman (BDH) assumption in the random oracle model. Comprehensive performance evaluation shows that our work has much efficiency of decryption, trapdoor generation and test execution thanks to greatly reducing the cost of trapdoor generation and thus is practical for the application of secure ciphertext information retrieval in cloud environment.

Flexible and secure continues data transmission among multiple users in cloud environment

Cloud computing is an internet based computing where the sharable information, software and resources are provided based on demand devices. Where, the rapid development and pervasive growth of unavoidable sending of message advances, there are expanding requests of adaptable cryptographic natives to protected group data transactions and computing platforms in cloud. Group key agreement (GKA) protocol enables a group to share a standard encryption key across an open network so that only members of the group may decode the ciphertexts encoded using the secret encryption key that has been released. However, a sender cannot deny any specific member from decryptions the ciphertexts in cloud. However, before sending a message to a group, a user must join the group and follow the GKA protocol to provide the intended members access to a secret key. To find a better solution for the above-mentioned issues, flexible and secure continues data transmission (FSCDT) algorithm is proposed to offer dynamic and secure data transfer broadcasting without full trust of key authority in unreliable cloud environment. It provides compete security proof, outlines the requirements of the aggregatability of the secret attribute based FSCDT building block. Based on experimental evaluations, FSCDT algorithm minimizes encryption time, decryption time and communication cost.

Task scheduling of cloud computing based on hybrid particle swarm algorithm and genetic algorithm

Task scheduling in cloud environment is a hot topic in current research. Effective scheduling of massive tasks submitted by users in cloud environment is of great practical significance for increasing the core competitiveness of companies and enterprises and improving their economic benefits. Faced with the urgent need for an efficient scheduling strategy in the real world, this paper analyzed the process of cloud task scheduling, and proposed a particle swarm optimization genetic hybrid algorithm based on phagocytosis PSO_PGA. Firstly, each generation of particle swarm is divided, and the position of the particles in the sub population is changed by using phagocytosis mechanism and crossover mutation of genetic algorithm, so as to expand the search range of the solution space. Then the sub populations are merged, which ensures the diversity of particles in the population and reduces the probability of the algorithm falling into the local optimal solution. Finally, the feedback mechanism is used to feed back the flight experience of the particle itself and the flight experience of the companion to the next generation particle population, so as to ensure that the particle population can always move towards the direction of excellent solution. Through simulation experiments, the proposed algorithm is compared with several other existing algorithms, and the results show that the proposed algorithm significantly improves the overall completion time of cloud tasks, and has higher convergence accuracy. It shows the effectiveness of the algorithm in cloud task scheduling.

A Hybrid Grey Wolf Optimizer and Artificial Bee Colony Algorithm Used for Improvement in Resource Allocation System for Cloud Technology

Cloud computing is the next generation of technology which provide different service with the rule of pay and gain with the help of internet. These services consist of hardware and software used in different field of life. Due the growth of user in cloud environment the number of access and share system of technology increases which causes different issue and resource allocation system is one of them. In this paper for improvement in resource allocation system in VM a hybrid algorithm used because in some situation VM become underloaded and overloaded in cloud data centre due to lack of proper load balancing technique system. Therefore a hybrid technique used for improvement in VM allocation system. The hybrid technique consist of GWO and ABC algorithm three main section of GWO technique improve first improvment occur at local search section in this section ABC algorithm local search technique used second improvement occur at fitness function along with the energy parameter. The above proposed technique used to improve four main parameter of scheduling which are energy consumption, throughput network stability and average network executation time in resource allocation system in VM for cloud computing. The proposed technique result are compare with ABC algorithm , GWO algorithm, RAA algorithm based on those result the proposed algorithm improve 1.25 % accuracy and efficiency for resource allocation system in VM for cloud computing.

Fitness rate-based rider optimization enabled for optimal task scheduling in cloud

ABSTRACT Not long ago, there has been a dramatic augment in the attractiveness of cloud computing systems that depends computing resources on-demand, bill on a pay-as-you-go basis, and multiplex many users on the same physical infrastructure. It is considered as an essential pool of resources, which are offered to users through Internet. Without troubling the fundamental infrastructure, pay-per-use computing resources are provided to the users by the cloud computing technology. Scheduling is a significant dilemma in cloud computing as a cloud provider has to serve multiple users in cloud environment. This proposal plans to implement an optimal task scheduling model in cloud sector as a challenge over the existing technologies. The proposed model solves the task scheduling problem using an improved meta-heuristic algorithm called Fitness Rate-based Rider Optimization Algorithm (FR-ROA), which is the advanced form of conventional Rider Optimization Algorithm (ROA). The objective constraints considered for optimal task scheduling are the maximum makespan or completion time, and the sum of the completion times of entire tasks. Since the proposed FR-ROA has attained the advantageous part of reaching the convergence in a small duration, the proposed model will outperform the other conventional algorithms for accomplishing the optimal task scheduling in cloud environment.

Energy Efficient Resource Allocation in Cloud Computing

Cloud service provider in cloud environment will provide or provision resource based on demand from the user. The cloud service provider (CSP) will provide resources as and when required or demanded by the user for execution of the job on the cloud environment. The CSP will perform this in a static and dynamic manner. The CSP should also consider various other factors in order to provide the resources to the user, the prime among that will be the Service Level Agreement (SLA), which is normally signed by the user and cloud service provider during the inception phase of service. There are many algorithm which are used in order to allocate resources to the user in cloud environment. The algorithm which is proposed will be used to reduce the amount of energy utilized in performing various job execution in cloud environment. Here the energy utilized for execution of various jobs are taken into account by increasing the number of virtual machines that are used on a single physical host system. There is no thumb rule to calculate the number of virtual machines to be executed on a single host. The same can be derived by calculating the amount of space, speed required along with the time to execute the job on a virtual machine. Based up on this we can derive the number of Virtual machine on a single host system. There can be 10 virtual machines on a single system or even 20 number of virtual machines on single physical system. But if the same is calculated by the equation then the result will be exactly matching with the threshold capacity of the physical system[1]. If more number of physical systems are used to execute fewer virtual machines on each then the amount of energy consumed will be very high. So in order to reduce the energy consumption , the algorithm can be used will not only will help to calculate the number of virtual machines on single physical system , but also will help to reduce the energy as less number of physical systems will be in need[2].

Credit Value and Attribute Based Access Control Mechanism for Cloud Data Centres

Now-a-days the cloud is very useful for providing many IT services. These services are delivered over the internet and accessed globally with the help of internet. The cloud service provider ensures flexibility in provisioning and scaling of resources. The cloud services are completely managed by cloud service provider (CSP), which ensures the end to end availability, reliability and security of the cloud resources. The exponential growth of cloud services has provided many opportunities but has also perplexed severe security concerns. The popularity of cloud service based applications is rapidly increasing due to which many security and legal issues are arising. In this paper we describe the existing access control method and framework for securing cloud services. The concept of modified reputation and attribute based access control system has been discussed. In this approach the concept of crowd reviewing has been used to compute the credit value of users. The simulation experiment has been shown to protect the consistent users and to restrict the access of inconsistent users in cloud environment. It is an access control approach to mitigate the challenges in security concerns. This access control mechanism is helpful for cloud application services, which automatically restrict the malicious users from the access of resources. It is also helpful in authorization of users to access the cloud resources.

Dependability Of The Users In Cloud Environment Usingload Balancing And Integrity Of Data

A distributed computing is one of the most amazing administration offered by means of web which makes a stage to store and recover information Since client's close to home information is being put away in a decoded arrangement on a remote machine worked by outsider merchants who give different administrations, the effect of client's personality and unapproved access or revelation of records are extremely high.In this proposed system is using Dependability of the Users,Privacy and Integrity of Data (DPI) algorithms using improve the Execution assessment of Cloud Computing foundations is required to anticipate and measure the money saving advantage of a technique portfolio and the relating Quality of Service (QoS) experienced by clients. Along these lines, concentrating on Load adjusting in the distributed computing condition importantly affects the presentation. This paper deals with multi target asset provisioning plan for taking care of different errand classes for different outstanding task at hand office. In this proposal, project is using a Best Partition Searching for distributing a file system to another cloud environment. This methodology gives a security regarding client verification for "approval" to enter the system which is made by means of Image Sequencing secret key to demonstrate that the personality is unique client, RSA calculation to encode the information to give information respectability, a profoundly parallel conveyed information the executives administration that empowers to peruse/compose and add gigantic informational collections that are divided and circulated at a huge scale. Subsequently this methodology gives a general security to the customer's close to home information and the issue of simultaneousness, volume of information can be settled with these procedures.

To optimize load of hybrid P2P cloud data-center using efficient load optimization and resource minimization algorithm

Cloud is the most trending technology used at almost in every part of the business and in every field of business. Cloud provides number of services in wider spectrum to cloud users from anywhere at any time. But, it achieved through several parameters like deployment model, resource optimization, load optimization etc. Nowadays, Load optimization is playing crucial role in cloud computing behalf system performance. The best optimization technique goal is to fulfill the user requirement efficiently with minimal resources and processing time. Parallel task processing is highly demanded in cloud application. The CPU resources are needed to move for parallelism growth due to communication and synchronization of parallel job arrival in cloud. It is difficult but highly demanded for a data center to response arrived task in parallel way. The objective of work is to design Efficient Load Optimization and Resource Minimization (ELORM) algorithm for optimizing the tasks at different Hybrid P2P Cloud data center zones and different users in cloud environment. The works provides an effective way to distribute the resources based on load prediction in the data centers for resource optimization. It enhances the load optimization, by maintaining the reliability and stability between the user base and data center during data transmission process. It also reduces the resource utilization and response time of the proposed algorithm compared than conventional methods. Proposed ELORM reduces 83.13 s Task completion time, 20.82 $ Virtual machine cost, 6.68% load balancing compare than conventional methods.

A novel key management mechanism using elliptic and Diffie-Hellman for handling users in cloud environment

A novel key management mechanism using elliptic and Diffie-Hellman for handling users in cloud environment

Multi-Layer Token Based Authentication Through Honey Password in Fog Computing

This article describes how fog computing empowers network resource utilization by providing services to low bandwidth users in cloud environment. Here, authentication mechanisms are used by fog nodes while providing services to end users. In cloud computing there are several types of authentication mechanisms, in which token based authentication takes less time for validating password. The proposed technique uses token based authentication mechanism with honey password. The aim of this article is to focus on multi-layer token based authentication for identifying the authorized user at fog node. In authentication phase end user instantaneously frame his user account password with honey password that makes illusion to shoulder surfers password has changed. The proposed mechanism avoids and detects shoulder surfing attacks, password guessing attacks, and application denial of service attacks.

An Efficient Resource Management for Prioritized Users in Cloud Environment Using Cuckoo Search Algorithm

Cloud computing is a model which is empowered for on demand and convenient system access to a shared pool of computing resources. The clients of the cloud can be an individual or an association, and can acquire their respective services from cloud service provider. Advances in technologies, lead to the relocation from antiquated desktop devices to smart mobile devices. With a large increase in the number of mobile devices and bandwidth clients can execute as many tasks from their gadgets itself. Be that as it may, with versatility come its innate issues, for example, resource scarceness, finite energy and low network connectivity. This is, indeed, not only a temporary technological inadequacy but intrinsic to mobility and a hindrance that needs to be run over. Here in this paper, a load-aware allocation strategy is proposed and allocation is considered as an optimization problem with the aim of reducing the make span and the computational cost meeting the deadline constraints, with high resource utilization and is solved using Cuckoo-Search algorithm. The proposed approach is evaluated using Cloudsim framework and the results showed that our approach works better than other metaheuristic algorithms.

A Cloud-Manager-Based Re-Encryption Scheme for Mobile Users in Cloud Environment: a Hybrid Approach

Cloud computing is an emerging computing paradigm that offers on-demand, flexible, and elastic computational and storage services for the end-users. The small and medium-sized business organization having limited budget can enjoy the scalable services of the cloud. However, the migration of the organizational data on the cloud raises security and privacy issues. To keep the data confidential, the data should be encrypted using such cryptography method that provides fine-grained and efficient access for uploaded data without affecting the scalability of the system. In mobile cloud computing environment, the selected scheme should be computationally secure and must have capability for offloading computational intensive security operations on the cloud in a trusted mode due to the resource constraint mobile devices. The existing manager-based re-encryption and cloud-based re-encryption schemes are computationally secured and capable to offload the computationally intensive data access operations on the trusted entity/cloud. Despite the offloading of the data access operations in manager-based re-encryption and cloud-based re-encryption schemes, the mobile user still performs computationally intensive paring-based encryption and decryption operations using limited capabilities of mobile device. In this paper, we proposed Cloud-Manager-based Re-encryption Scheme (CMReS) that combines the characteristics of manager-based re-encryption and cloud-based re-encryption for providing the better security services with minimum processing burden on the mobile device. The experimental results indicate that the proposed cloud-manager-based re-encryption scheme shows significant improvement in turnaround time, energy consumption, and resources utilization on the mobile device as compared to existing re-encryption schemes.

Enhanced dynamic credential generation scheme for protection of user identity in mobile-cloud computing

To improve the resource limitation of mobile devices, mobile users may utilize cloud-computational and storage services. Although the utilization of the cloud services improves the processing and storage capacity of mobile devices, the migration of confidential information on untrusted cloud raises security and privacy issues. Considering the security of mobile-cloud-computing subscribers' information, a mechanism to authenticate legitimate mobile users in the cloud environment is sought. Usually, the mobile users are authenticated in the cloud environment through digital credential methods, such as password. Once the users' credential information theft occurs, the adversary can use the hacked information for impersonating the mobile user later on. The alarming situation is that the mobile user is unaware about adversary's malicious activities. In this paper, a light-weight security scheme is proposed for mobile user in cloud environment to protect the mobile user's identity with dynamic credentials. The proposed scheme offloads the frequently occurring dynamic credential generation operations on a trusted entity to keep minimum processing burden on the mobile device. To enhance the security and reliability of the scheme, the credential information is updated frequently on the basis of mobile-cloud packets exchange. Furthermore, the proposed scheme is compared with the existing scheme on the basis of performance metrics i.e. turnaround time and energy consumption. The experimental results for the proposed scheme showed significant improvement in turnaround time and energy consumption as compared to the existing scheme.