Hybrid Techniques for Shared File Security in Cloud Environment Using SHA-256

Virtual computing resources are provided via a cloud system that is both clever and intelligent. Based on the user's request, computing resources are made available. A hybrid cloud is the best option for storing and accessing user data for cloud deployments. Maintaining security in a hybrid cloud environment is time-consuming. This study provides a novel strategy for securing data in the hybrid cloud by ensuring the user's data is protected. Users' data in a hybrid cloud is protected using cryptographic approaches provided in this approach. Using this strategy, users' data may be protected in public and private clouds using various encryption methods. The suggested data security paradigm offers various advantages to both consumers and providers in terms of data security. Three symmetric encryption methods are offered as a service in the cloud. The concept is implemented as a cloud-based application hosted in the cloud, and the effectiveness of three strategies is assessed. They are evaluated in terms of performance and security. Using the recommended encryption methods in a hybrid cloud environment is more efficient than using other methods. The proposed technique can be used for relational data. It can be modified and enhanced to process multimedia data.

Comparative Study of Security Algorithms in Cloud Computing

Cloud computing relies on sharing computing resources. With high availability and accessibility of resources, cloud computing is under the threat of major cyber-attacks. To detect attacks and preserve security in cloud environment, having an efficient intrusion detection system (IDS) is required. In this article, an effective and efficient IDS is proposed to maintain high level security of data in cloud. The authors have incorporated Reinforcement Learning Automata with their proposed IDS while detecting and classifying attacks. Using learning automata an effective rule set is generated with the proposed algorithm from vast training set to improve the learning process at reduced computation cost and time. After which, the proposed reinforcement learning algorithm helps in classification of attacks accurately using the reinforcement signal. This proposed model was experimented with NSL-KDD as well as KDD 10% dataset and have proved its robustness by detecting attacks more accurately being an IDS.

In the modern era of the computing world, the data producing and using it is becoming large and instant at various places. For availing the data at different locations for processing we need to store it in the global platform. Cloud environment provides a best and easy way for this. Cloud computing is becoming as the essential thing for high-quality data services. However there are some potential problems with respect to data security. Here encryption techniques can be used for providing security, but with restricted efficiency. In this paper we propose a new encryption mechanism for providing data security in cloud environment. We propose a two round searchable encryption which supports multi keyword retrieval. Here we adapted a vector space model for improving search accuracy; the elgamal encryption technique allows users to involve in the ranking, while the essential key part of encryption will be done at the source itself. The proposed improves the data security and reduces data leakage.

Cloud computing develop quickly, which has powerful computing ability and allows authorized users to store and transmit data on cloud servers. Therefore, the security of applications in the cloud environment becomes an inevitable challenge. Therefore, many scholars at home and abroad have studied how to improve the security in cloud environment. The emergence of attribute-based encryption (ABE) has made it a better choice to improve cloud security. Subsequently, since ciphertext attribute-based encryption (CP-ABE) is more suitable for cloud storage than key attribute-based encryption (KP-ABE). Therefore, in view of the efficiency and security of CP-ABE, the CP-ABE’s access structure and revocation mechanism are studied in depth. Finally, we summarizes the weaknesses of the existing ABE schemes and points out the future research directions. So the paper provides a reference for further research.

In the rapidly evolving landscape of digital services, cloud computing has become a cornerstone for data storage, processing, and transfer, significantly amplifying security challenges. This environment faces numerous threats and security issues, including cyberattacks, data breaches, and malware, in addition to challenges related to ensuring confidentiality, integrity, and availability. This study aims to highlight the effective role of machine learning techniques in enhancing cloud security by detecting cyber threats, protecting data privacy, and preventing attacks. It underscores the pressing need to adopt intelligent solutions, given the limitations of traditional systems in addressing the complex and evolving threats within cloud environments. Through a critical review of recent research on this topic, the study examines various machine learning methods proven effective in tackling key cloud security issues, such as network intrusion detection, financial fraud detection, and malware identification. The findings confirm that machine learning techniques significantly improve the efficiency of security systems. However, challenges remain, including adapting to continuously evolving threats, ensuring the quality of available training data, and addressing the high computational costs of real-time model operation. The study recommends further research to enhance cloud security, improve adaptability to emerging threats, and establish a theoretical foundation to guide future efforts toward strengthening security in cloud environments.

As cloud environments become integral to infrastructure projects, maintaining security and regulatory compliance is paramount, particularly in blue-green deployments that prioritize continuous delivery and reduced downtime. This paper explores the integration of Artificial Intelligence (AI) to enhance data-driven compliance and security measures in cloud-based blue-green infrastructure projects. AI can identify vulnerabilities, enforce compliance, and predict risks by analyzing massive datasets in real-time, enabling more secure and resilient deployments. Through a review of current practices and case studies, this paper demonstrates how AI can streamline compliance audits, detect anomalies, automate security policies, and ensure adherence to industry standards in blue-green environments. The findings offer a blueprint for leveraging AI to meet evolving regulatory requirements and mitigate security threats in dynamic cloud infrastructures.

This Nowadays system hear that CLOUD computing is ubiquitous. Most of tech giants have converted their owned in-house data centers to using CLOUD technology and outsourcing the part of their services to public cloud and to opt for hybrid cloud architecture. There is no doubt that CLOUDs have the potential for being the next generation model computational services, but with that potential comes the risk of theft and concerns related to security and privacy. A lot of development and progress has already been made in CLOUD technologies, there still remains a wide range of concerns this article system identify and classify the main security concerns and solutions in cloud computing, and propose a method of security in cloud environment. In this paper Row Column Diagonal (RCD) based engine has been proposed. Along with the cloud sub-cloud. Data Security using engine in Environment is also explained. Further, engine system has been proposed in Central Cloud and RCD Supervisor modes in environment along with the situation of uploading and downloading. Validation and analysis have been performed for Engine and indicates based engine is much powerful and secure. As Algorithm is much more robust and easy to implement with considering all the aspects of main and sub cloud using supervisor as the central body.

Security in cloud environments is paramount due to the increasing prevalence of Distributed Denial of Service (DDoS) attacks, which can severely disrupt services and cause significant financial and data losses. Traditional security mechanisms, such as Intrusion Detection Systems (IDS) and Firewalls, often struggle to detect and mitigate novel, evolving threats due to the lack of predefined detection signatures. This paper proposes a real-time signature-based detection mechanism tailored specifically for cloud environments. The proposed system generates signatures in real-time, enabling the identification and prevention of emerging DDoS attacks. The effectiveness of the solution is validated through extensive experimental evaluations, demonstrating its ability to reduce attack impact and enhance cloud security.

Cloud storage is used generally to store data and realize the data sharing with others. A cloud storage system stores large number of data in its storage server. Data that are stored to the cloud have to be secured in order to avoid the data from exploiting. There are several techniques and methods that are used to secure the data before uploading it to the cloud such as cryptographic encryption techniques. In this paper, we study about different encryption technique to protect the cloud storage environment. This paper covers some of the existing cryptographic approaches that can be used to improve the security in cloud environment.

The security issues in the cloud have been well studied. The data security has much importance in point of data owner. There are number of approaches presented earlier towards performance in data security in cloud. To overcome the issues, a class based multi stage encryption algorithm is presented in this paper. The method classifies the data into number of classes and different encryption scheme is used for different classes in different levels. Similarly, the user has been authenticated for their access and they have been classified into different categories. According to the user profile, the method restricts the access of user and based on the same, the method defines security measures. A system defined encryption methodology is used for encrypting the data. Moreover, the user has been returned with other encryption methods which can be decrypted by the user using their own key provided by the system. The proposed algorithm improves the performance of security and improves the data security.

Cloud computing is a nascent paradigm in the field of data technology and computer science which is predicated on the use of the Internet, often known as the World Wide Web. One of the prominent concerns within this field is the security aspects of cloud computing. Contrarily, ensuring the preservation of access to the protection of sensitive and confidential information inside financial organizations, banks and other pertinent enterprises holds significant significance. This holds significant relevance. The efficacy of the security measures in providing assurance is not infallible and can be compromised by malevolent entities. In the current study, our objective is to examine the study about the security measures through the use of a novel methodology. The primary objective of this research is to investigate the subject of data access in the realm of cloud computing, with a particular emphasis on its ramifications for corporations and other pertinent organizations. The implementation of locationbased encryption facilitates the determination of accurate geographical coordinates. In experiment apply Integrated Location Based Security using Multi objective Optimization (ILBS-MOO) on different workflows and improve performance metrics significantly. Time delay averagely approximates improvement 6-7%, storage 10-12% and security 8-10%.

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The problem of public auditing has been well studied in many research articles. The methods described earlier suffer to achieve higher result in public auditing. To overcome the challenges in public auditing, the author propose a hierarchical attribute based encryption based modular padding scheme. The method organizes the cloud data in hierarchical manner and each level of attributes has been stored in semantic manner. The semantic relations of the attributes are used to select the key parameter and the same is used to encrypt or decrypt the cloud data. The semantic ontology maintains the category of data attributes and for each data attribute the ontology refers a secret key. The key from the semantic ontology is selected and the same is used to perform verification. Again the encrypted data is performed with modular padding, which selects the random number and based on the value of random number, a padding of 1 or 0 is performed. The proposed approach improves the performance of public auditing and security in cloud environment.

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.