Comparative Analysis of Data Security and Cloud Storage Models Using NSL KDD Dataset

Cloud computing is becoming increasingly important in many enterprises, and researchers are focusing on safeguarding cloud computing. Due to the extensive variety of service options it offers, A significant amount of interest from the scientific community has been focused on cloud computing. The two biggest problems with cloud computing are security and privacy. The key challenge is maintaining privacy, which expands rapidly with the number of users. A perfect security system must efficiently ensure each security aspect. This study provides a literature review illustrating the security in the cloud with respect to privacy, integrity, confidentiality and availability, and it also provides a comparison table illustrating the differences between various security and storage models with respect to the approaches and components of the models offered. This study also compares Naïve Bayes and SVM on the accuracy, recall and precision metrics using the NSL KDD dataset

Cyberattacks and security of cloud computing: a complete guideline

Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, key exposure, auditing, privacy preservability, and cloud-assisted IoT applications. We then propose security attacks and countermeasures specifically for the different cloud models based on the security trends and problems. In the end, we pinpoint some of the futuristic directions and implications relevant to the security of cloud models. The future directions will help researchers in academia and industry work toward cloud computing security

Stealth databases : ensuring user-controlled queries in untrusted cloud environments

Sensitive data is increasingly being hosted online in ubiquitous cloud storage services. Recent advances in multi-cloud service integration through provider multiplexing and data dispersion have alleviated most of the associated risks for hosting files which are retrieved by users for further processing. However, for structured data managed in databases, many issues remain, including the need to perform operations directly on the remote data to avoid costly transfers. In this paper, we motivate the need for distributed stealth databases which combine properties from structure-preserving dispersed file storage for capacity-saving increased availability with emerging work on structure-preserving encryption for on-demand increased confidentiality with controllable performance degradation. We contribute an analysis of operators executing in map-reduce or map-carry-reduce phases and derive performance statistics. Our prototype, StealthDB, demonstrates that for typical amounts of personal structured data, stealth databases are a convincing concept for taming untrusted and unsafe cloud environments

A Protection Layer over MapReduce Framework for Big Data Privacy

In many organizations, big data analytics has become a trend in gathering valuable data insights. The framework MapReduce, which is generally used for this purpose, has been accepted by most organizations for its exceptional characteristics. However, because of the availability of significant processing resources, dispersed privacy-sensitive details can be collected quickly, increasing the widespread privacy concerns.  This article reviews some of the existing research articles on the MapReduce framework's privacy issues and proposes an additional layer of privacy protection over the adopted framework. The data is split into bits and processed in the clouds, and two other steps are taken. Hadoop splits the file into bits of a smaller scale. The task tracker then allocates these bits to several mappers. First, the data is split up into key-value pairs, and the intermediate data sets are generated.  The efficiency of the suggested approach may then be effectively interpreted. Overall, the proposed method provides improved scalability. The following figures compare execution time with relation to file size and the number of partitions. As privacy protection technique is used, the loss of data content can be appropriately handled.  It has been demonstrated that MRPL outperforms current methods in terms of CPU optimization, memory usage, and reduced information loss.  Research reveals that the suggested strategy creates significant advantages for Big Data by enhancing privacy and protection. MRPL can considerably solve the privacy issues in Big Data

A Systematic Review on Image Data Protection Methods

Securing data is the main goal of any data security system (DSS). Valuable data must be protected all the time and stored in a very highly secure data storage device. This need has become more critical due to the continuous growth of data size.  Furthermore, non-text data in the form of images, audio, and videos can now be transferred and processed easily and thus become part of sensitive data that needs to be protected. Since there is a need to secure and protect data in any form in order to keep them private and valid, it is expected that there would be many attempts already that have been proposed in the literature for this purpose. This paper reviews a group of these proposed strategies and methods that have been applied to different kinds of DSSs. Challenges and future trends of DSSs are also discussed. A number of main findings are grouped and organized as follows: (1) there are many different kinds of security techniques, each of which offers varying degrees of performance in terms of the amount of data and information that can be managed securely, (2) depending on the architecture of the proposed method, the tactics or strategies of the security system, the kinds of DSSs, as well as a few other factors, some methods are more appropriate for the storage of certain categories of data than others

Cyberattacks and Security of Cloud Computing: A Complete Guideline

Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, key exposure, auditing, privacy preservability, and cloud-assisted IoT applications. We then propose security attacks and countermeasures specifically for the different cloud models based on the security trends and problems. In the end, we pinpoint some of the futuristic directions and implications relevant to the security of cloud models. The future directions will help researchers in academia and industry work toward cloud computing security

Secure data sharing in cloud computing: a comprehensive review

Cloud Computing is an emerging technology, which relies on sharing computing resources. Sharing of data in the group is not secure as the cloud provider cannot be trusted. The fundamental difficulties in distributed computing of cloud suppliers is Data Security, Sharing, Resource scheduling and Energy consumption. Key-Aggregate cryptosystem used to secure private/public data in the cloud. This key is consistent size aggregate for adaptable decisions of ciphertext in cloud storage. Virtual Machines (VMs) provisioning is effectively empowered the cloud suppliers to effectively use their accessible resources and get higher benefits. The most effective method to share information resources among the individuals from the group in distributed storage is secure, flexible and efficient. Any data stored in different cloud data centers are corrupted, recovery using regenerative coding. Security is provided many techniques like Forward security, backward security, Key-Aggregate cryptosystem, Encryption and Re-encryption etc. The energy is reduced using Energy-Efficient Virtual Machines Scheduling in Multi-Tenant Data Centers

Implementation of Dynamic Virtual Cloud Architecture for Privacy Data Storage

Nowadays rapidly developing technologies, cloud computing offers versatile services. However, cloud computing presents a challenge to secure information sharing. Customers can securely share their data with others and remotely store it in the cloud using cloud storage services. In recent times, cloud storage typically represents as the primary method of external data storage. The primary challenge is safeguarding the cloud-based data against attacks. Over the information network, the growth of private or semi-private information has increased. The search techniques have not been addressed by privacy safeguards. As there is no suitable audit system, the validity of the stored data has become in question. In addition, user authentication presents additional difficulties. Hence in order to solve these issues, Design and implementation of dynamic virtual cloud architecture for privacy data storage is presented. In this approach, third-party audits are presented accompanied a new, regenerative public audit methodology. A distributed KDC (Key Distribution Center) is employed to encrypt the data. Documents can be stored on a private server in plain word form, which compromise the protection of privacy. As a result, system security can be improved to make the documents safer and more effective. The main objective of this Virtual Cloud Architecture is to achieve data confidentiality, as well as authenticity.&nbsp