Enhancing Cloud Infrastructure Security Through AI-Powered Big Data Anomaly Detection

'Big data' analytics can be defined by the requirement for flexible, high throughput computational analysis methods applied to large, heterogeneous datasets. We propose an architectural approach to 'big data' challenges in which the movement of data is minimized, and analysis methods are implemented on the data as portable services. We term this approach 'data interlocking'. We demonstrate the feasibility of this approach through a domain specific implementation of a data interlocking architecture, in which an on-demand computational workbench provides portable high-throughput analysis methods to large genomic datasets on cloud infrastructure.

In the era of big data, the cloud infrastructure needs to strongly support big data. As a distributed computational framework, Hadoop is one of the de facto leading software tools for solving big data problems. The cloud infrastructure has been proven to be a good support for three-tier architecture applications. In this paper, we construct a Hadoop big data platform based on OpenStack cloud. At the same time, we design three experimental scenarios, carry out a set of experiments using the standard Hadoop benchmarks TestDFSIO, TeraSort and PI, and examine the performance. Our experiments reveal that the disk read operation of physical servers can be a bottleneck for TestDFSIO and TeraSort. Wider allocation of VMs over physical servers achieves better performance for read jobs of TestDFSIO and TeraSort. For CPU-intensive job PI, the best practice is to centralize the allocation of VMs over physical machines.

\nPurpose: In the past, big data was the concern of governments and large commercial industries, and so these organizations had infrastructures separate from the public network to store and process their data. But today, big data is easily accessible to everyone through cloud infrastructure. In recent years, with the explosive development of the Internet, data storage and data processing technologies, privacy has been one of the major concerns in data mining\n
\n\nMethodology: In this paper, after examining the dimensions and key components of flax data, privacy in big data; Blockchain solution for privacy protection is examined.\n
\n\nFindings: This topic provides new opportunities for researchers in knowledge processing tasks. However, these opportunities often bring challenges as well.\n
\n\nOriginality/Value: Big Data means a very large database that can be structured or unstructured. Big data is a database that is constantly getting bigger with the increase of information. Also with the rapid advancement of the Internet, data storage and data processing technologies, big data privacy has attracted a lot of attention. Before applying big data technology to mass applications and applications, a basic issue called privacy must be explored.\n

As cloud computing continues to gain traction, a great deal of effort is being expended in researching the most effective ways to build and manage secure and trustworthy clouds. Providing consistent security services in on-demand provisioned Cloud infrastructure services is of primary importance due to the multi-tenant and potentially multi-provider nature of Cloud Infrastructure. Cloud security infrastructure should address two aspects of the IaaS operation and dynamic security services provisioning: (1) provide security infrastructure for secure Cloud IaaS operation; (2) provisioning dynamic security services. Although the first task is a traditional task in security engineering, dynamic provisioning of managed security services in virtualized environment remains a problem and requires additional research. Entire frameworks have been proposed and demonstrated but although successful, there is a tendency to see such solutions as integrated 'all in one' infrastructures. This paper describes a light-weight mechanism and protocol for building trust between two machines that takes advantage of the Trusted Platform Module (TPM) to handle a key exchange and remote trusted deployment of a bootstrapping tool (referred to as the Bootstrapping Initiator (BI)). Once deployed, the BI can execute any arbitrary software required which could be (but is not limited to) solutions for advanced architecture management such as the Dynamic Access Control Infrastructure (DACI). The proposed solution provides a light-weight layer of trust backed by a TPM that additional systems can build upon as required by the individual use case without the requirement for a specific management or security infrastructure to be deployed along with it.

Security and privacy issues are magnified by the volume, variety, and velocity of Big Data, such as Large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition and high volume inter-cloud migration. In the past, Big Data was limited to very large organizations such as governments and large enterprises that could afford to create and own the infrastructure necessary for hosting and mining large amounts of data. These infrastructures were typically proprietary and were isolated from general networks. Today, Big Data is cheaply and easily accessible to organizations large and small through public cloud infrastructure. The purpose of this chapter is to highlight the Big Data security and privacy challenges and also presents some solutions for these challenges, but it does not provide a definitive solution for the problem. It rather points to some directions and technologies that might contribute to solve some of the most relevant and challenging Big Data security and privacy issues.

Today's computing world is facing tsunami and driving without riding on this tsunami towards next generation computing is no choice. So many IT companies decided to grow up with this tsunami like technology. One of these is cloud computing and another is Big data. Currently more than 5 billion mobile users and nearly same facebook and other social media user generate this tsunami of data. On another side to deliver this services of big data a model called as cloud computing is spreading everywhere as next generations IT Service model. Both technologies continue to evolve. Ultimately as a cloud computing development matures, every top mind of organizations will think for development of efficient and agile cloud environment. At the other side every cloud provider offers the services to the huge number data processing companies that generate data process data and make decision on cloud infrastructure. Ultimately its today's need to think on futures efficient cloud based Big data analytics In this review paper we are focusing on, how we can club Big data and cloud Computing in one frame of development.

This study investigates proactive monitoring and security within cloud infrastructure, focusing on implementing tools like Prometheus, Grafana, and HashiCorp Vault to enhance operational resilience and data security. As cloud adoption grows, so does the complexity of maintaining secure systems amidst ever-evolving threats and regulatory pressures. By integrating these tools within DevOps workflows, organizations can achieve real-time monitoring, visualization, and secure access management, significantly strengthening their cloud environments. A systematic literature review was conducted, examining each tool's unique capabilities, the limitations they address, and the potential for combined functionality to offer a layered security approach. The findings reveal that while these tools are invaluable for preemptively detecting threats and managing access, they require continuous refinement to meet scalability, compatibility, and compliance demands. This study concludes that a proactive, integrative approach utilizing Prometheus for data collection and alerting, Grafana for visualization, and HashiCorp Vault for secret management fosters robust cloud security practices. Recommended strategies include adopting zero-trust architectures, utilizing automated compliance tools, and integrating sustainability principles in cloud management to ensure both security and operational efficiency. Ultimately, this study emphasizes the need for adaptive security frameworks that evolve alongside technological advancements, positioning organizations to safeguard their digital assets and respond to emerging threats with agility and confidence.

This study delves into the current state and future potential of Big Data within Omani institutions, with a specific focus on insights provided by information and archives specialists. Employing a qualitative approach, the research conducts semi-structured interviews with 15 experts representing a diverse array of twelve Omani institutions, encompassing both public and private sectors. Participants were meticulously chosen based on their roles in records and archives management, supplemented by the involvement of IT specialists and administrative managers. The findings illuminate a nuanced understanding of Big Data within Omani institutions. While there exists a significant level of comprehension among staff regarding the complexities of Big Data, notable gaps persist in its practical application. Participants perceive Big Data as vast, diverse, and challenging data to manage, aligning with established scientific definitions. Formal education emerges as a critical avenue for knowledge acquisition, reflecting the evolving landscape where academic and professional domains intersect. Furthermore, interviews reveal the diverse nature of data within organizations, necessitating holistic management strategies and strategic investments in technology and security infrastructure. Key challenges such as data security, legal compliance, and the need for skilled human resources are underscored, emphasizing the necessity for specialized solutions and training programs. Moreover, the discussion extends to the future trajectory of records and information management, emphasizing the imperative of adapting to technological advancements and the continuous pursuit of education. Overall, this study offers valuable insights into the awareness, applications, transformations, governance procedures, and challenges associated with Big Data management within Omani institutions, as perceived by information and archives specialists.

Big Data and Smart Governance are important components in achieving sustainable governance. This article explores the integration of Big Data in public administration and its role in improving government efficiency, transparency, and accountability through data-driven decision-making. While Big Data offers great opportunities, governments face a variety of challenges, including data privacy, security, and technology infrastructure issues. This article identifies these challenges and highlights the potential of Smart Governance in supporting sustainable development by leveraging technology to achieve environmental, economic, and social goals. Through a comprehensive analysis, this study presents policy recommendations to help governments and stakeholders make optimal use of Big Data in supporting more effective and sustainable governance. These findings highlight the long-term benefits of integrating Big Data in public administration to ensure equitable and sustainable development.

Data is precious! Data generated from myriad resources are store and treated, extracted for getting knowledge through classical machine learning techniques. The MapReduce is a programming framework that proved its efficiency and reliability for big data processing in a distributed environment. When data are generated in a cloud platform the speed and volume of data are high. MapReduce has its own limitation over processing big data. Spark has also eliminated many boundaries especially dealing with online streaming and cloud generated data. Big Data cluster management is a tedious work that needs specialized algorithms for a fair share approach. Many time resource contention problems have to address with fair-share but the performance was not satisfactory. MESOS and YARN have an implementation of the same. The proposed framework use historical job execution logs for admission control and deadline information is an additional and important part which supplied at the time of submission of the job. The work anticipates different deadline formulation explained in the result section. Finally, a comparison of justice with existing fair share allocation policies is discussed.

The rapid growth of data production across diverse fields created big data as an essential area for both academic study and practical application during recent times. The spread of cloud computing provides businesses with an adaptable infrastructure that enables economical and scalable processing and storage of enormous data collections. Popular use of modern data mining methods requires dealing with major technical obstacles while analyzing massive and complicated information collections. Data mining as an intelligent methodology emerged because it unites artificial intelligence methods with machine learning techniques together with statistical approaches and database operations to resolve these difficulties. In this review paper demonstrates extensive details about Intelligent data mining solutions that operate within cloud infrastructure while handling big data. The paper presents essential details about the basic principles followed by a classification scheme which includes techniques like classification and clustering alongside association rule mining with deep learning methods and their applications for big data needs. This study analyzes data mining system integration models for clouds while it assesses their operational abilities and scalability and makes note of essential difficulties including heterogeneity and real-time processing requirements.

The digital revolution has changed the way, that business is conducted in many areas. Recently, large volumes of various types of data are used at high speed by a variety of sources, that are processed, often in real time, using information technology tools (powerful processors, software and algorithms. Today’s bank servers contain vast amounts of data collected from online interactions. People in modern times willingly share information about themselves. Traditional banking analytics systems aren’t capable of processing huge data sets from various sources. However, the development of digital technologies and big data analytics allow bank clients to structure and use their personal information to solve existing problems of saving bank data. It was determined, that every client of the bank is worried about the preservation of his personal information, which is provided to the bank managers. The essence of the concept of cloud computing, cloud infrastructure is defined, and the purpose of using cloud interfaces is also considered. Levels of problems, related to the security of saving bank customer data, have been revealed. The levels of opportunities for using the cloud pyramid to increase the level of security of big data in banking activities have been determined. The cloud enables the operation of an ideal platform with massive computing power and storage capacity to process big data with great variety, volume, veracity and speed. The SaaS provider takes care of the performance of applications, provides technical support for users, and installs updates independently. The type of services and the type of organizations, to which Software as a Service (SaaS) technologies can be applied, are considered. The role of Software-as-a-service (SaaS) technologies to increase the level of big data security in banking is defined.

Human-intelligence workflow management for the big data of augmented reality on cloud infrastructure

The article highlights the increasing complexity of modern threats, extending beyond traditional military challenges to include economic stability, cybersecurity, critical infrastructure protection, and resilience to hybrid threats. The growing interconnectivity and digitalization of the global environment necessitate new security strategies that leverage advanced technologies and international cooperation. This study emphasizes the significance of integrating innovative technological solutions such as artificial intelligence (AI), big data analytics, blockchain, unmanned systems, and the Internet of Things (IoT) into national security frameworks. Leading nations worldwide, including the United States, Israel, and China, have been at the forefront of developing and implementing these advanced strategies, enabling them to predict, mitigate, and respond to diverse security threats more effectively. The article provides a comprehensive analysis of international best practices in deploying innovative security measures. It focuses on how advanced technologies enhance threat detection, crisis management, and the protection of critical infrastructure. For instance, AI's role in threat prediction and the automation of cybersecurity measures, the use of blockchain for data transparency and protection, and the deployment of unmanned systems for border surveillance and infrastructure security are explored in detail. The adaptation of these approaches in the Ukrainian context is essential for developing a robust national security strategy that can withstand contemporary global challenges. Ukrainian researchers have also been actively exploring the application of these technologies to improve national defense capabilities. The article reviews the contributions of scholars such as O. Lytvynenko, O. Voronin, and I. Romanenko, who have analyzed various aspects of cybersecurity, crisis management, and hybrid threats. The integration of international experiences, adapted to local conditions, is seen as a crucial step toward enhancing Ukraine's national security framework, ensuring its readiness to address emerging challenges effectively. The study concludes that adopting an innovative, multi-dimensional approach to security, which incorporates traditional defense mechanisms and modern technological tools, is key to building a comprehensive and resilient national security system.

<p>Internet of Things (IoT) requires cloud infrastructures for data analysis (e.g., temperature monitoring, energy consumption measurement, etc.). Traditionally, cloud services have been implemented in large datacenters in the core network. Core cloud offers high-computational capacity with moderate response time, meeting the requirements of centralized services with low-delay demands. However, collecting information and bringing it into one core cloud infrastructure is not a long-term scalable solution, particularly as the volume of IoT devices and data is forecasted to explode. A scalable and efficient solution, both at the network and cloud level, is to distribute the IoT analytics between the core cloud and the edge of the network (e.g., first analytics on the edge cloud and the big data analytics on the core cloud). For an efficient distribution of IoT analytics and use of network resources, it requires to integrate the control of the transport networks (packet and optical) with the distributed edge and cloud resources in order to deploy dynamic and efficient IoT services. This paper presents and experimentally validates the first IoT-aware multilayer (packet/optical) transport software defined networking and edge/cloud orchestration architecture that deploys an IoT-traffic control and congestion avoidance mechanism for dynamic distribution of IoT processing to the edge of the network (i.e., edge computing) based on the actual network resource state.</p>