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Design of a mathematical fusion model for enhanced cloud data security
This paper proposes an effective quantum computing method that integrates secure cloud access and quantum key distribution (QKD) to ensure secure access to cloud resources. The proposed method employs a quantum circuit to implement a secure cloud access protocol that utilizes QKD to distribute a secret key between the cloud user and the service provider. The secret key encrypts all parties’ access requests, response messages, and data. The security of the proposed method is analyzed against various types of attacks, including intercept-resend, man-in-the-middle, and eavesdropping. The simulation results indicate that the proposed method offers significant protection against such attacks. It can potentially provide a secure cloud access platform for various applications, including sensitive data storage, healthcare data management, and other applications that require a high level of security.
Rapid development and massive use of Information Technology (IT) have since produced a massive amount of electronic data. In tandem, the demand for data outsourcing and the associated data security is increasing exponentially. Small organizations are often finding it expensive to save and process their huge amount of data, and keep the data secure from unauthorized access. Cloud computing is a suitable and affordable platform to provide services on user demand. The cloud platform is preferable used by individuals, Small, and Medium Enterprises (SMEs) that cannot afford large-scale hardware, software, and security maintenance cost. Storage and processing of big data in the cloud are becoming the key appealing features to SMEs and individuals. However, the processing of big data in the cloud is facing two issues such as security of stored data and system overload due to the volume of the data. These storage methods are plain text storage and encrypted text storage. Both methods have their strengths and limitations. The fundamental issue in plain text storage is the high risk of data security breaches; whereas, in encrypted text storage, the encryption of complete file data may cause system overload. This paper propose a feasible solution to address these issues with a new service model called Confidentiality-based Classification-as-a-Service (C2aaS) that performs data processing by treating data dynamically according to the data security level in preparation for data storing in the cloud. In comparison to the conventional methods, our proposed service model is strongly showing good security for confidential data and is proficient in reducing cloud system overloading.
Cloud computing is defined as the distribution of computing including hardware and software to the consumer through the Internet. In the era of ICT, cloud computing has been influenced by many industries including technology, business, management, logistics and numerous other industry. But some new kinds of risks and vulnerabilities exist in cloud environment. Users of cloud services are under constant threat. Hence, security-related risks are the main disadvantage of cloud computing. The aim of this paper is to enhance the cloud security by designing a secure cryptosystem based on AI. We have emphasized on secure key generation algorithm based on coupled artificial neural network with Mealy machine, genetic algorithm and weight vector-based authentication mechanism. We have used coupled multilayer feedforward neural network, Mealy machine and genetic algorithm for key generation. Machine learning is done ‘n’ times between two ANNs, and after several steps, we have generated a secret key for encryption. A novel key wrapping protocol has also been introduced using one-way function. For encryption and decryption, we have used the concept of isomorphism in vector space and XOR operation with double encryption key. Thus, our paper is equipped with different types of new concepts. Varieties of experimental results and analysis prove the efficiency and robustness of our technique in the field of cryptography.
Cloud computing is defined as the management and provision of resources, software, applications, and information as services over the internet. Numerous organizations are adopting cloud services because of the economy and technology changes. Over the past year, cloud computing has made life easier for other people or organizations by allowing people to have access to information from anywhere in the world through the internet. However, cloud computing introduces a series of security concerns, which data security is a major concern. Data security is basically linked with any security in the cloud. If the network or infrastructure security is compromised, the possibility there is a possibility of data being affected. Data security has three important principles that are Confidentiality, Integrity, and Availability (CIA) that needs to always be maintained. Understanding the security concerns in cloud computing is an important requirement for those who are planning to move to the cloud solution. Thus, this paper aims to investigate and develop a hybrid data security framework to mitigate data security challenges in the cloud, focusing on legal frameworks such as POPIA and GDPR. A survey was conducted to investigate which techniques are more effective in protecting data and the evaluation ensured that CIA triads are covered. The limitation on the existing techniques is that they address only one principle or two, but not all three at the same time. The best technique among the others will be used integrated when designing the proposed framework in order to address CIA. Having more than one security technique to address data security will improve the security in the cloud and will also bring more business to cloud service providers when customers see the security in data enhanced.
Network function virtualization (NFV) has gained prominence in next-generation cloud computing, such as the fog-based radio access network, due to their ability to support better QoS in network service provision. However, most of the current service function chain (SFC) deployment researches do not consider the Security-Service-Level-Agreement (SSLA) in the deployment solution. Therefore, in this work, we introduce the SSLA into SFC deployment to defend attacks. Firstly, we formulate the SSLA guaranteed SFC deployment problem by using linear programming. Then, we propose the Maximal-security SFC deployment algorithm (MS) to maximize the security of the SFC deployment. However, the MS algorithm results in a high deployment cost. To reduce the deployment cost, we propose the Minimal-cost and SSLA-guaranteed SFC deployment algorithm (MCSG) to minimize the deployment while satisfying the SSLA. In order to reduce the blocking ratio caused by MCSG, the Minimal-cost and SSLA-guaranteed SFC deployment algorithm with feedback adjustment (MCSG-FA) is proposed. Finally, we evaluate our proposed algorithms through simulations. The simulation results show that the blocking ratio and the deployment cost of our algorithms are better than that of the existing algorithm when meeting the SSLAs.
Several new technologies such as the smart cities, the Internet of Things (IoT), and 5G Internet need services offered by cloud computing for processing and storing more information. Hence, the heterogeneity of the new companies that used the above-mentioned technologies will add many vulnerabilities and security concerns for the cloud paradigm. Presently, cloud computing involves every component such as end-user, networks, access management, and infrastructures. Without a lucid vision of the cloud infrastructure, security communities struggle with problems ranging from duplicating data to failing to identify security threats in a timely way, with loss of control about protection and data access to face regulatory compliance. With cloud computing becoming part of our everyday life and our digital computer environment,
we look forward to rapid new development in the computational needs provided by cloud computing paradigms. In this paper, we first provide an architecture tutorial on cloud computing technology, including their essential characteristics, services models, deployment models, and cloud data center virtualization. Second, we provide the cloud computing security issues and frameworks, and through a comprehensive survey, we characterize and summarize the efforts made in the literature to find solutions to these security issues. Third, we categorize the various attacks in the cloud and privacy challenges. Fourth, we summarize the efforts made in the literature to the defense mechanisms and mitigation solution for security assessment. Finally, we discuss open issues in cloud security and propose some future directions.
Cloud storage service providers caters to the need of organizations and individuals by allowing them to store, transfer and backup their ever-increasing amount of data at low cost along with providing access to the other resources of cloud. For providing efficient data storage, cloud service providers utilize most widely employed deduplication technique as it allows storage of single instance of data and removes duplicate copies of data, thus mitigating storage overhead and saving upload bandwidth. Clients uploading their data on cloud are most concerned about the security, integrity, privacy and confidentiality of their data. Conventional encryption usually employed to encrypt data while outsourcing it, is not recommended as it conflicts with data deduplication technique and so in most cases, Convergent Encryption (CE) and Proof of Ownership (PoW) are used to protect confidentiality and integrity of data. Several other approaches such as Provable Data Possession (PDP), Proof of Retrievability (POR), secure keyword search, DupLESS, Proof of Storage with Deduplication (PoSD), Dekey, Message-Locked Encryption, Attribute Based Encryption (ABE) and Identity Based Encryption (IBE) have been researched to address client’s security concerns and this paper does a literature review on such various proposed a
Cloud computing has become the most attracting day to use for the largest scope association at present or for the person who needs various organization administration with small expense. Cingular's data on a regular basis is kept on the open cloud that is accessible for all to access. This raises some of the fundamental issues in contrast to the adaptable administration offered by cloud suppliers, such as confidentiality, integrity, availability, authorization, and more. As of late, there are a lot of options available for securing information and the best way is to use encryption. Encryption will not be able to provide enough protection, considering the fragile data of the customers. This additionally burns away more opportunity to perform encryption and decoding measures for each query. Furthermore, it is not a good practice to consider client-operated in light of the fact that once client's information is transferred to the cloud premises, the client does not have direct power over this information. Thinking about this reality we should consider the security of the client's critical data on the cloud worker.
An increased use of data driven applications and integrated systems have caused an accelerating expansion in data volumes and increase in the number of digital records, over the past few decades. Exponentially growing data volumes being processed by large-scale distributed data-intensive applications have placed an increasing pressure on the underlying storage services for timely and efficient storage and retrieval of the data. The use of cloud storage is among the best strategies to efficiently store growing volumes of data. However, outsourcing data to public cloud storage leads to the challenge of data confidentiality preservation. Data Confidentiality is among the top challenges associated with cloud storage which have contributed substantially as an inhibitor for cloud computing adoption all over the world and is considered a serious concern, especially in case of big data, where securing data in a timely and accurate manner is an arduous task. Our study aims to contribute in anti-cybercrime by protecting the confidentiality of sensitive growing data. We enunciate an optimized confidentially preserving framework on distributed cloud storage, that works for growing data with time-efficiency and minimum memory usage. Our framework uses a merger of Genetic Algorithm (GA), parallel data distribution, and privacy-aware selective encryption techniques. The experiments and comparative analysis depict that our proposed framework outperforms others under consideration, in terms of execution time, memory usage and network throughput respectively.
Internet of Things (IoT) is a materializing technology that has considerable technical, social, and economic importance. The present day predictions for the influence of IoT are highly monumental. This is because data of any form is found to be predominant to the IoT paradigm, therefore serving different applications like smart home, smart city, healthcare, smart grid, smart farms, manufacturing, and so on. Hence, several heterogeneous sensors are said in existence to estimate different types of parameters and found to be useful for different users. In this paper, a secured image sharing method called a Blockchain Chaotic and Paillier Map-based Authentication (BC-PMA) to provide image data integrity and security by deploying a cloud computing environment is proposed. The BC-PMA method is split into three sections. First, registration of corresponding classes of images in the cloud server via blockchain is presented by employing the Cat Map Registration model. The second step involves the authentication process carried out by employing Paillier Map-based Authentication where validation is ensured via smart contracts. Finally, upon successful authentication, unique and separate classes of images are provided with the respective, therefore ensuring secured classes of image sharing between users in the cloud environment. The results have been analyzed against traditional blockchain-based methods and validated with enhanced simulated results, that good authentication accuracy, minimizing the false positive rate, block generation time, and size considerably. Experimental Result shows that the Authentication Accuracy of the proposed BC-PMA method is improved by 23%, and minimizes the False Positive Rate and Computational Cost by 32% and 22% when compared to the Existing Method.
Conducting enterprise distributed computing and providing facilities such as data storage and resource sharing, along with the provision of cheap, easy, and flexible services, have led to the increasing popularity of cloud computing. Data migration and applications outside the administrative domain of customers cause numerous security challenges in cloud computing. Investigating the security challenges, vulnerabilities, and threats in different network layers is important due to the significant role of security in cloud computing. In this paper, the existing challenges of different network layers in cloud computing will be identified, analyzed, and classified. Categorizing security challenges in cloud computing from the network layers’ perspective considering service models, cloud service providers and cloud users leads to useful methods for system designers to provide a systematic approach for a deeper understanding and then, detecting and preventing security risks. Emerging solutions and preventive methods that may potentially reduce vulnerabilities in various network layers are also introduced in this paper.
At present, the research of virtual machine thermal migration phenomenon in project engineering has become a widespread concern of experts and scholars, especially the detection of cloud computing security problems such as data leakage and economic loss. This paper proposes a set of data anomaly detection and response scheme. Based on LOF anomaly detection algorithm, this scheme introduces traceability mechanism and adaptive theory to improve it, and establishes adaptive DR-LOF algorithm (Dimensional Reasoning Local Outlier Factor) to realize real-time dynamic anomaly detection of data. At the same time, it uses Analytic hierarchy process algorithm to establish an anomaly response hierarchy model to protect the stability and integrity of cloud computing platform. The detection rate of LOF algorithm before and after improvement is 10% and 70% respectively, and the false alarm rate is 40% and 20% respectively. The simulation results show that the% inset/s dimension index has the minimum LOF value in the case of virtual machine migration without injecting abnormal attack, while the rcpck/s dimension index has the minimum LOF value in the case of injecting abnormal attack. However, the change of K value has little effect on the anomaly detection performance of adaptive DR-LOF algorithm. The research results have great value and potential significance in the field of cloud computing security event detection and response.
Social networking and growing popularity of cloud services have made everyone to communicate each other in an easiest way. File sharing and distribution are the frequently used services provided by cloud service providers, although these facilities reduce cost of data sharing but at the same time data security and access control is the major problem. Many renowned service providers have faced the challenges to secure data and provide better access control, and we know once the data is leaked we cannot recover the data loss. Thus in order to ensure better security we need for focus on the two major problems, and those are access control and encryption policy. Cipher text policy attribute based encryption is the most effective solution for access control in real time scenarios where owner can actually decide the access rights for the end-user, but it comes with key escrow problem. We are proposing our modified escrow-free key issuing protocol to solve the problem of key escrow and our Modified Attribute Based Encryption scheme to achieve all security requirements to get a robust and secure system. Further we evaluate our model on the basis of results and lastly we conclude the paper.
Enhanced Security Architecture for Cloud Data Security
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