A Hybrid Approach to Secure Cloud Storage: Machine Learning and Blockchain Integration

According to Cisco Global Cloud Index, cloud storage users will store 1.6 Gigabytes data per month by 2019, compared to 992 megabytes data per month in 2014. With this trend, it has been shown that more and more data will reside in cloud storage and it is expected to grow further. As cloud storage is becoming an option for users for keeping their data online, it comes with security concerns for protecting data from threats. This thesis addresses the need to investigate the security factors that will enable efficient security protection for data in cloud storage and the relationships that exist between the different security factors. Consequently, this research has developed a conceptual framework that supports security in cloud storage. The main contribution of this research is the development of a Cloud Storage Security Framework (CSSF) to support an integrative approach to understanding and evaluating security in cloud storage. The framework enables understanding of the makeup of security in cloud storage and measures the understanding of security in cloud storage. Drawing upon established theories and prior research findings, the framework indicates that security in cloud storage can be determined by nine factors: (1) security policies implementation in cloud storage, security measure that relates to (2) protecting the data accessed in cloud storage; (3) modifications of data stored; (4) accessibility of data stored in cloud storage; (5) non-repudiation to the data stored; (6) authenticity of the original data; (7) reliability of the cloud storage services; (8) accountability of service provision; and (9) auditability of the data accessed and stored in cloud storage. An example of CSSF application has been demonstrated through the development of a measuring instrument called Security Rating Score (SecRaS) and through a series of experiments, SecRaS has been validated and used in a research scenario. The instrument consists of several items generated using goal-question-metric approach. These potential items were evaluated by a series of experiments; the security experts assessed using content validity ratio while the security practitioners took part in the validation study. The validation study completed two experiments that look into the correlation analyses and internal reliability. SecRaS instrument was later applied in a research scenario; the validated instrument was distributed and a number of 218 usable responses were received. Using structural equation modelling, the data has revealed a good fit of the measurement analyses and structural model. The key findings were as follow: the relationships between factors were found to have both direct and indirect effects in the result. While establishing the relationship(s) among the factors, the structural model proposes three types of causal relationships in terms of how the security implementation in cloud storage could be affected by the security factors. This thesis presents a detailed discussion of the CSSF development, confirmation, and application in a research scenario. For security managers, CSSF offers a new paradigm on how stakeholders can make cloud storage security implementation successful in some depth. For security practitioners, the CSSF enables deconstruction of the concept of security in cloud storage into smaller, conceptually distinct and manageable factors to guide the design of security in cloud storage. For researchers, the CSSF provides a common framework in which to conceptualise their research and make it easier to see how the security factors fit into the larger picture.

Financial fraud continues to threaten the integrity of digital financial systems, with traditional rule-based detection methods increasingly ineffective against evolving tactics. Recent advances suggest that integrating Machine Learning (ML) and Blockchain Technology may provide a robust solution. This study explores how this hybrid approach can improve fraud detection in financial transactions by enhancing accuracy, transparency, and response efficiency. The study adopted a quantitative research design using a publicly available dataset of over 2,500 financial transactions. Features included transaction amounts, account behavior, login attempts, and timestamps. Supervised machine learning models—Random Forest and Support Vector Machine (SVM)—were applied to classify transactions as fraudulent or legitimate. The models were trained, optimized, and evaluated using metrics such as accuracy, precision, recall, and F1-score. Descriptive analysis revealed fraud accounted for only 6.8% of transactions, confirming significant class imbalance. The Random Forest model outperformed the SVM, achieving 99.9% accuracy, precision, recall, and F1-score. TransactionAmount, TransactionDuration, and CustomerOccupation were found to be the most influential predictors. The integration of blockchain was identified as vital for secure, immutable data storage, enabling real-time auditability and enhancing the trustworthiness of the machine learning process. The combination of machine learning’s predictive power with blockchain’s immutable ledger creates a highly effective fraud detection framework. Random Forest was identified as the superior model in terms of both performance and reliability for this application. Financial institutions should adopt integrated ML-blockchain systems to strengthen fraud prevention, ensure transaction transparency, and support regulatory compliance. This study contributes to the advancement of intelligent and secure financial systems by offering empirical evidence of the value of this hybrid approach. Keywords: Fraud Detection, Machine Learning, Blockchain, Random Forest, Financial Transactions, Cybersecurity, Anomaly Detection.

With the idea of new developing technologies and the rapid change in our technical environment, there is a need for smarter, more efficient technology to keep up with the needs of today's world. Thus, the idea of smart cities has come into being. The purpose of this chapter is to promote the idea of building a smart city and providing a framework for future development of the same. We are moving towards a more futuristic world where everything is connected and done over the internet, which will create more network dependency. The problem with the concept of smart cities is the need for better technologies to improve the system of data transfer to address the problem of trust in the system, as massive amounts of people's private data is at stake with bad actors trying to hack the system to gain access to it. There is also the problem of the massive amounts of data produced in smart cities, so there is a need to provide a framework to efficiently store all the data. These problems can be solved with the help of technologies such as blockchain and cloud computing. Cloud computing can be seen as a way of sharing resources offering compute, storage, databases, and network services that can be used on a larger scale. Cloud computing stores massive amounts of data, it can also be used to share and transfer data between networks in a cloud, it is cost-effective, and you can also retrieve lost data or backup data when needed. Despite cloud computing being so great, it also has its own drawbacks. The problem of security in cloud storage is ever-increasing; since cloud storage, centralized systems have been a major target for hackers and data breaches, and the data in the cloud can be tampered with and accessed by unauthorized users. If data is being transferred through the cloud it can be intercepted during transmission because of the problem of encryption. These problems can be solved as well as the system being improved and made faster with the help of blockchain. Blockchain is a database or ledger that is shared across a network. The ledger is encrypted so that only people who are authorized can access the data. Blockchain will decentralize the data stored, which will improve the security of the data immensely. If a hacker targets a single block in a node, it will not have much effect and will continue to function. Block chain works by breaking the data into smaller chunks, and the chunks are distributed in a way that even if the network is down the data will still be available. While a file is being shared it is broken into smaller pieces and sent in a way that the file transfer is quicker. The files are encrypted using a cryptographic hash table and each individual shard or part of the file is also encrypted, making the security even tighter. With the help of blockchain, the storage of files will become much cheaper and more secure. A framework is provided to manage and secure the data acquired from IoT devices through encryption using Advanced Encryption Standard (AES) algorithm and storing the data in a private or public cloud depending on the data; and then decrypting the data and sending it to the users who requested the data or for further analysis. This chapter provides a framework for a smart city storage and security model and can be used as a prototype model for future development in the field.

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

Modern technological advances have given rise to the popularity and success of cloud in organizations and industries because it provides data storage facility and computing services at minimal cost. Data security is one of the important requirements for outsourced data in a cloud storage environment. It also brings new security vulnerabilities for assuring the data confidentiality, data integrity and data access control. Data security of a cloud system depends heavily on the cloud service provider, data owner and cloud users. So, there needs to be some methodology to monitor security vulnerabilities and providing data security in cloud environment. Now a day’s providing data security in cloud storage is one of the most critical tasks. For the past few years many works have been done to assure data security but they are failed in providing data security such as information leakage, secret-key management, cloud-user authentication, breach of data confidentiality, because of collusion attack and lots of computational activities (because of large number of secret keys).In this paper, we used threshold cryptography technique for providing data security in cloud storage system. Proposed approach addresses the data security issues in cloud system more efficiently. In this Threshold cryptography technique data owner create users in groups based on place or location, project, and department, data owner allocates single secret key to each group of users for decryption of data and every users in the group shares parts or pieces of the secret key. Proposed methodology also uses data access control list to control the data access. Proposed approach provides data security more efficiently, it also increases the performance of the system and also it minimizes the number of secret keys.

With the development and application of cloud computing, cloud storage is becoming more and more popular with many organizations, but traditional principal-minor structure for cloud storage is at risk of single point of failure. Moreover, it has brought safety risk. Cloud storage security has become the key problem of restricting the development of cloud computing. The paper has a deep research of cloud storage structure and cloud storage security, proposes a entity authentication protocol for P2P cloud storage based on ternary Huffman Merkle hash tree (HuffMHT).This method using the concept of ternary HuffMHT can obtain an effective safe strategy. At the same time, symmetrical key algorithm and public key algorithm are just combined to reduce the authentication delay effectively and increase the network lifetime and enhances the security of the networks. Moreover, signatures of knowledge is used in this paper, which notes number of hash is reduced, the power which notes consume is debased, the network load is decreased. The performance of the system is discussed, and the encryption method has high efficiency and high security.

The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.

Cloud computing provides a scalable platform for large amount of data and processes that work on various applications and services by means of on-demand service. The storage services offered by clouds have become a new profit growth by providing a comparable cheaper, scalable, location-independent platform for managing users’ data. The client uses the cloud storage and enjoys the high end applications and services from a shared group of configurable computing resources using cloud services. It reduces the difficulty of local data storage and maintenance. But it gives severe security issues toward users’ outsourced data. Data Redundancy promotes the data reliability in Cloud Storage. At the same time, it increases storage space, Bandwidth and Security threats due to some server vulnerability. Data Deduplication helps to improve storage utilization. Backup is also less which means less Hardware and Backup media. But it has lots of security issues. Data reliability is a very risky issue in a Deduplication storage system because there is single copy for each file stored in the server which is shared by all the data owners. If such a shared file/chunk was missing, large amount of data becomes unreachable. The main aim of this work is to implement Deduplication System without sacrificing Security in cloud storage. It combines both Deduplication and convergent key cryptography with reduced overhead.

Using cloud data clients may transfer data from their computer systems to cloud servers. Hence, user will not have any burden of maintenance and also he gets high quality data storage services. Many security issues are concerned with cloud storage. Cloud service providers or storage servers are not completely trustworthy. The user is concerned about the information stored on cloud are in place or not turns. This paper is based on homomorphic hash algorithm. Further this supports dynamic operations such as insert, update, delete and modify at block level, for data dynamics Merkle Hash Tree is used which helps to find the location of each dynamic operation. Third party auditor checks the user's data for correctness and gives the accuracy of the data that is stored in cloud server. The communication and computation overhead are reduced. Deduplication technique is used to check whether the file that user need to store in cloud storage is already exist at cloud server or not. This framework is effective and secure against replace attack launch by malicious server.

In cloud storage the data clients can remotely store their data and use on-demand high- quality applications. Data outsourcing users are reassured from the trouble of data storage and maintenance when users put their data in huge size on the cloud, the data integrity protection is a challenging one which enabling public audit for cloud data storage security. Users can ask an external inspection of third party to check the integrity of the out sourced data. However, in such a scheme, the malicious cloud might still forge valid authenticators later than the key-revelation time period if it obtains the current secret key of Data Client. In this paper, propose a prototype named Robust Key Revelation of public auditing for secure cloud storage, in which the security of cloud storage auditing not only earlier than but also later than the key revelation can be preserved. Formalized the definition and the security model of this new kind of cloud storage auditing and design a concrete scheme. In this proposed prototype, the key revelation in one-time period doesn’t affect the security of cloud storage auditing in other time periods. The accurate security proof and the experimental results demonstrate that this proposed prototype achieves desirable security and efficiency with the time intervals. Keywords: Secure Cloud Storage, Robust Key Revelation, Security, Data Client, and Public Auditing.

The demand for cloud computing is increasing rapidly because of the advantages it provides to the customers like, pay as you use, self-serving, elastic, sharing of resources, ease of use, and accessibility. Due to the increase in the usage of the technology, there exists a high chance of compromising the security of the data being stored on the cloud. The major hindrance in the usage of the technology is the security concerns which accompany it. This increases the demand for a robust security mechanism to protect the data on the cloud. So as to overcome this drawback of cloud computing, encrypting the data to be stored on the cloud is one of the solutions. As part of this paper, a security mechanism to improve the security of data in cloud storage is suggested. The security mechanism used is inspired by the bio-inspired genetic algorithm. The inspiration behind the proposed security model is an amalgamation of genetic algorithm and attribute based encryption. As per the methodology proposed the data need to be encrypted before being stored on the cloud. This way the cloud service provider is unaware of the data being stored and even if the data is compromised to some third party, there is no information leakage.

Security of cloud storage has drawn more and more concerns. In the searchable encryption, many previous solutions can let people retrieve the documents containing single keyword or conjunctive keywords by storing encrypted documents with data indexes. However, searching documents with a phrase or consecutive keywords is still a remained open problem. In this paper, using the relative positions, we propose an efficient scheme LPSSE with symmetric searchable encryption that can support encrypted phrase searches in cloud storage. Our scheme is based on non-adaptive security definition by R. Curtmola and with lower costs of transmission and storage than existing systems. Furthermore, we combine some components of currently efficient search engines and our functions to complete a prototype. The experiment results also show that our scheme LPSSE is available and efficient.

<span lang="EN-US">There is a growing influence around the use of technology in education, with many solutions already being implemented and many others being explored. Using various forms of technology to assist the educational process has increased dramatically in the previous decade in education systems in many respects. Both machine learning and the blockchain have had a significant impact on education. The purpose of this study is to conduct a literature review on the application of machine learning and blockchain technology in educational institutions. Additionally, this study examines the potential applications, benefits, and challenges those educational institutions may face as a result of using machine learning and blockchain technologies. Using machine learning and blockchain in educational systems will have a positive impact on the entire educational process and student achievement. Researchers, academics, and practitioners will benefit from this study to focus on a wider range of educational applications and solve the related issues of machine learning and blockchain technology in the education sector.</span>

Applications have rapidly transformed the data access, business operations, and communication methods. This sudden shift has resulted in significant security challenges such as Distributed Denial of Service (DDoS) attacks, which intensify Internet security issues. This paper introduces a novel approach to enhancing the Web Application Firewall (WAF) for detecting and mitigating botnet-based DDoS attacks through the use of Machine Learning (ML) and blockchain technologies. Legacy security systems often struggle to adapt to evolving digital threats, particularly with the rise of complex botnet designs. The integration of ML and blockchain within the WAF ecosystem represents a substantial advancement in cyber defense mechanisms. Insights are provided into the development of advanced ML algorithms for precise anomaly detection and the formulation of efficient blockchain protocols for streamlined threat intelligence sharing. The proposed approach addresses current challenges associated with botnet-driven DDoS attacks establishes a foundation for adaptive, future-proof cybersecurity strategies.

The purpose of this research is to empirically examine the effect of third party auditor and quality service to cloud storage and user trust. This research is causal quantitative and use partial least square and path analysis. The research object is corporation users of cloud computing storage in listed corporation in Indonesia Stock Exchange. The sample size is 368 corporations and the results of the research are as follow: third party auditor has positive effect on both cloud storage security and cloud user trust. Quality of service has positive effect on both cloud storage security and cloud user trust. Cloud storage security has positive effect on cloud user trust.