A Multi-layered Protection System for Enhancing Data Security in Cloud Computing Environments

Cloud computing solutions are being gradually adopted in organizations, and this has raised a lot of questions about the risks that come with using cloud-based services, especially misconfiguration errors in cloud-based systems. Considering these challenges, the current study aimed to assess the use of AI-based security systems to identify misconfiguration errors in cloud computing platforms. The study has focused on the perceived effectiveness of implementing AI security systems for behavioral analysis, and the implementation of traditional security systems has been assessed. The study utilized an exploratory qualitative review approach analyzing the recent studies and the recent literature available on recognized databases of interest with a particular focus on the prevalence of misconfiguration errors, its importance for early detection, and the use of AI methods. The study results showed that the most significant threat and the main reason for cloud insecurity and breaches lie in cloud misconfiguration. However, the analysis of the study also exposed that AI and machine learning techniques have a promising future and could be enacted as an accurate technique for the automation of the early misconfiguration detection model. Furthermore, the research has also pointed out that planning and deception-based approaches and human factors analysis can also be used in an attempt to strengthen cloud security against misconfiguration threats. In general, the study has postulated that the enhancement of optimum AI-based security systems is fundamental to optimizing the misconfiguration error detection systems and the security of data in cloud computing.

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date: September 30 th 2022

In this paper, we present a management system for distributed software development in cloud computing environments, called DDMan. Cloud computing environments provide more flexibility than conventional computing environments. In particular, the platform as a service (PaaS) in cloud computing environments provides benefits for users in a variety of aspects, such as application design, development testing, software deployment, team collaboration, web service integration, database integration, and scalability. Although the process of distributed software development evolves and changes by a lot in the past decade, there are still some user needs remaining unsatisfied. For managing distributed software development in cloud computing systems, we present the DDMan system, which extends the WebSD model of distributed software development management. In DDMan, the interactions among the roles in the software development are precisely defined. In addition to the conventional web-based interface, the DDMan system also supports the interface for the Amazon cloud environment, which is an approach to a fully automatic service of distributed software development management in cloud computing environments.

Based on the hierarchical architecture of computing, this paper discusses the confidentiality, integrity and usability of multi-dimensional security system in light of user dimension, dimension, business dimension and basic dimension. According to this division, this paper puts forward specific security technologies and strategies. Through the use and management of the of the main (cloud computing users and service providers) and implementation environment (network hardware environment and Social system environment) into the security off the system, as much as possible to grasp the computing in the application and promotion process in all aspects of the problem, thus a comprehensive, comprehensive trusted cloud system is established. In the discussion of multi-dimensional system model, each dimension of security protection has its own emphasis. By using the trust management technology, the user authentication and authorization system based on trust management are put forward. Data dimension and data encryption technology research, design and implementation of a storage security solutions. Taking service-oriented security audit and monitoring as the focus, this paper puts forward service-oriented security monitoring system. The basic dimension focuses on network and application system host security. In addition, a standardization evaluation system for security is put forward to improve the multi-dimensional protection system for safety standardization.

This project establishes a Chinese wall security policy model in the environment of cloud computing. In 1988 Brewer and Nash proposed a very nice commercial security policy in British financial world. Though the policy was well accepted, but the model was incorrect. A decade later, Dr. Lin provided a model in 2003 that meets Brewer & Nash’s Policy. One of the important components in Cloud computing is data center. In order for any company to store data in the center, a trustable security policy model is a must; Chinese wall security policy model will provide this assurance. The heart of the Chinese Wall Security Policy Model is the concept of Conflict of Interest (COI). The concept can be modeled by an anti-reflexive, symmetric and transitive binary relation. In this project, by extending Dr. Lin’s Model, we explore the security issues in the environment of cloud computing and develop a small system of the Chinese Wall Security Model.

In China, with the continuous advance of cloud computing technologies such as virtualization technology and big data processing technology, small and medium-sized enterprises are constantly updating and improving their management information system. In recent years, computer network technology and information technology have been gradually applied to enterprise management. With the development of enterprises, a large number of heterogeneous software services deployed on different servers are accumulated in enterprises. Cloud computing can not only reduce the cost of informatization construction of small and medium-sized enterprises, strengthen the integration of information resources, promote resource sharing, but also promote enterprise management and service innovation. In view of the development of social economy and the advance of the industry, if the enterprise wants to be better based on the market, it must realize the scientific management mode, and constantly improve the efficiency and quality of work. Through the cloud computing model, this paper introduces the system construction of the management information system for small and medium-sized enterprises in the cloud computing environment and the implementation scheme of enterprises in the cloud computing environment.

The cloud computing is an Internet-based resource sharing system in which virtualized resources are provided over the Internet. Cloud computing refers to a class of systems and applications that employ distributed resources for use in various applications; these computing resources are utilized over a network to facilitate the execution of tasks. However, cloud computing resources are heterogeneous and dynamic, connecting a broad range of resources. Thus, there are a large numbers of application and data center in the cloud computing environment. Therefore, the security issues of authentication and communication in application services and data center need to be considered in the cloud computing environment.

In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.

In the evolving landscape of biomedical biometrics, where multimodal approaches are increasingly crucial for reliable user authentication, this research presents a comprehensive study. The primary focus is on the construction and performance evaluation of a robust big data prediction model within a cloud computing environment. The advent of big data and cloud computing has revolutionized the field of biomedical biometrics, offering immense potential for advanced data analysis and prediction. This research presents the development and evaluation of a robust prediction model for multimodal biometric data in biomedical applications. The proposed model incorporation of Reliable Discrete Variable Topology (RDVT) into the prediction model. RDVT introduces a topological data structure that enhances data reliability and ensures the integrity of multimodal biometric information. The construction and training of the prediction model are meticulously detailed, encompassing data preprocessing, feature extraction, clustering, classification, and model evaluation. Additionally, the integration of a fuzzy clustering algorithm enhances the model's ability to handle uncertainty and imprecision in biometric data. The advancement of multimodal biometrics in the biomedical field by introducing the Reliable Discrete Variable Topology (RDVT) and a big data prediction model based on a fuzzy clustering algorithm in a cloud computing environment. The model's performance is rigorously assessed through extensive experimentation, including accuracy, precision, recall, and F1-score measurements.

This paper concentrates on how to mine useful information from massive XML documents in cloud computing environment. The structure of the Cloud computing and the corresponding tree data model of a XML document are analyzed in advance. Afterwards, structure of the proposed XML data mining system is illustrated, which is made up of three layers, such as “Application layer”, “Data processing layer”, and “XML Data converting layer”. In the XML Data converting layer, XML data are collected from databases and documents, and then the source data can be converted to XML file effectively. In the data processing layer, the process of data selection, cleaning and standardization for XML data set is implemented, moreover, a XML data set with higher degree of structure and rich semantics are obtained. In the application layer, “the results report module”, “data query module” and “results analysis module” are included. Next, massive XML data mining algorithm is proposed. The main innovations of this algorithm lie in that 1) the structure of a XML document is represented as an unordered tree, 2) the sub-structures of a XML document are modeled as sub-trees, and XML trees are regarded as a forest which is made up of all the sub-trees. Experimental results show that the proposed method can effectively mine useful information from massive XML documents in cloud computing environment with high efficiency.

Computers play essential roles in variety of fields to make their timely work efficient, with the help of high-performance computing architecture like Distributed Computing, Grid architecture, Cluster and Cloud technology. Cloud Computing is experiencing rapid development in both Academic and Industry. A Cloud Environment is a computing model with diverse capabilities which enables users with appropriate and need based access to reciprocated pool of computing resources like networking facility, storage servers, application servers and other services. Essential part of Cloud environment is proficient allocation of resources to user with suitable task. The Cloud Computing Environment has anthology of uncountable nodes, variety of resources, with potential challenges in task scheduling and execution. This paper presents a systematic study on Cloud Computing Environment with an assortment of optimization factors and its functionality in Cloud Computing.KeywordsCloud ComputingTask schedulingOptimization factorsTask allocation

The number of digital health records made each day will likely keep increasing a lot. Medical images have important details that help doctors make correct diagnoses and start treatments early. Securing medical images in a cloud computing environment is important. Medical images contain sensitive patient information. This information must be protected to keep it private and safe. This paper systematically reviews the current literature from leading academic databases including (ReseachGate, ScienceDirect, Scopus) to examine need for Medical Images security in Cloud Computing Environments. The findings reveal that cloud systems store data online, which makes it easier to access but also more vulnerable to security threats. To protect medical images, strong security measures are needed. These measures include encryption, access control, and regular monitoring. Proper security ensures that only authorized personnel can view or modify the images. It also helps prevent data breaches and unauthorized access. Protecting medical images in the cloud is necessary to maintain patient confidentiality and trust. This paper reviews and classify the issues, challenges and needs for securing medical images in cloud computing environments which a hot area that needs to be addressed by future research.

Virtualization is very important factor to reduce cost of data warehouse deployment. It can reduce cost as well as tremendous pressure of managing devices, storage servers, application models and man power. In current era, data warehouse is very effective concept that can make huge impact in organizational decision support system. Unlike database systems, data warehouse systems take large amount of time and cost to deploy and develop in-house system for an organization. This is the reason that people now think about cloud computing as a solution of the problem instead of implementing their own data warehouse system. This paper is about, how cloud computing environment can be established as an alternative of data warehouse system. It will also throw some light about better environment choice for the organizational need. Organizational data warehouse and EC2 (Elastic Cloud Computing) are discussed with different parameters like ROI, security, scalability, robustness of data, maintenance of system etc. Paper will help in comparative analysis of both the systems.

 Abstract—Cloud computing is an emerging technology that is still unclear to many security problems. Ensuring the security ofstored data in cloud servers is one of the most challenging issues in such environments. Accordingly, this schema presents a Hybrid Encryption algorithm that is based on RSA Small-e and Efficient RSA (HE-RSA) for improving the reliability in cloud computing environments. The main aim of this project is to use the cryptography concepts in cloud computing communications and to increase the security of encrypted data in cloud servers with the least consumption of time and cost at the both of encryption and decryption processes.In the proposed model, the number of key generation exponents has been increased in comparison to the original RSA. Moreover,a dual encryption process has been applied in this algorithm to prevent common attacks against RSA algorithm. In addition, this schema also presents the comparison between HE-RSA and original RSA in terms of security and performance. For this purpose, Key Generation, Encryption and Decryption Time in Original RSA and HE-RSA have been compared according to the different size of exponents. Moreover, some of the common attacks against RSA algorithm have been analysed to detect the resistance of the proposed algorithm against possible attacks. Finally, the effects of using HE-RSA on cloud computing environments have been reviewed in more depth and details.

With the development of extended enterprise and information technologies (IT), a new business pattern with its infrastructure, cloud computing, is emerging. More and more small and medium enterprises do not implement significant parts of their information systems (IS) in-house. Instead, they prefer to use the software services and even infrastructure services provided by professional information service companies. Their business strategy, IT strategy, business processes and information technologies shall be re-aligned. Furthermore, no cloud computing service vendor can satisfy the complete functional information system requirements of an enterprise. Sometimes, enterprises have to simultaneously use software services distributed in different clouds in conjunction with their intra-IS. These bring great challenges for business–IT alignment of an enterprise in the cloud computing environment (CCE). This study reviews business–IT alignment problems and models in CCE. The concept of collaboration point (CP) is proposed, and a CP-based business processes collaboration modelling technique is developed to improve the feasibility of IS in CCE. A framework to integrate applications and services deployed in public clouds and intra-IS is designed and a run-time platform with the collaboration agent technique is developed to realise the concept of CP. The case study illustrates the implementation of techniques developed in this article.