Cloud Storage Research Articles

Balancing the Load and Scheduling the Tasks using Zebra Optimizer in IoT based Cloud Computing for Big-data Applications.

Task scheduling is one of the major problems with Internet of Things (IoT) cloud computing. The need for cloud storage has skyrocketed due to recent advancements in IoT-based technology. Sophisticated planning approaches are needed to load the IoT services onto cloud resources professionally while meeting application necessities. This is significant because, in order to optimise resource utilisation and reduce waiting times, several procedures must be properly configured on various virtual machines. Because of the diverse nature of IoT, scheduling various IoT application activities in a cloud-based computing architecture can be challenging. Fog cloud computing is projected for the integration of fog besides cloud networks to address these expectations, given the proliferation of IoT sensors and the requirement for fast and dependable information access. Given the complexity of job scheduling, it can be difficult to determine the best course of action, particularly for big data systems. The behaviour of zebras in the wild serves as the primary basis of stimulus for the development of the Zebra Optimisation Algorithm (ZOA), a novel bio-inspired metaheuristic procedure presented in this study. ZOA mimics zebras' feeding habits and their defence mechanisms against predators. Various activities are analysed and processed using an optimised scheduling model based on ZOA to minimise energy expenditures and end-to-end delay. To reduce makespan and increase resource consumption, the technique uses a multi-objective strategy. By using a regional exploratory search strategy, the optimisation algorithm may better utilise data and stays out of local optimisation ruts. The analysis revealed that the suggested ZOA outperformed other well-known algorithms. It was advantageous for big data task scheduling scenarios since it converged more quickly than other techniques. It also produced improvements of 18.43% in several outcomes, including resource utilisation, energy consumption, and make span.

Online Pricing and Resource Scheduling for Profit Maximization of Cloud Storage Providers

Online Pricing and Resource Scheduling for Profit Maximization of Cloud Storage Providers

A Systematic Literature Review on Digital Forensic Investigation on Android Devices

Mobile forensics, particularly in the Android ecosystem, is a rapidly evolving field that demands continuous advancements to address the growing complexity and diversity of mobile devices. This article emphasizes the importance of developing techniques for digitally analyzing Android smartphones, which dominate the smartphone market. The primary objective of this research is to contribute to the development of effective forensic investigation strategies tailored specifically for Android mobile devices, providing insights into the tools and methods used for this purpose. The objective of this study is to improve the precision and effectiveness of forensic examinations pertaining to Android mobile phones. It discusses the fundamental functionality of mobile devices as a source of digital evidence and provides an overview of tools and methodologies for collecting and analyzing such evidence. The importance of comprehending the hardware and software architecture of Android handsets in order to choose the right forensic tools is also highlighted in the article. Furthermore, it proposes future enhancements for Andriller, a popular digital forensic tool, to improve its effectiveness in Android forensic investigations. These enhancements include advancements in data extraction techniques, compatibility with new Android versions, support for additional data types, integration with advanced analysis methods, and addressing identified limitations. Additionally, the paper stresses the need for robust methodologies for conducting cloud forensics on Android devices, particularly in the context of data stored in cloud storage services. The proposed work aims to enhance the capabilities of Andriller and improve the efficiency of digital forensic investigations on Android devices.

Research on abnormal access control of database management system under cloud computing platform

Abstract Access control is a security technique that can restrict access to protected resources, and data to only authorized users. In this paper, we design a blockchain-based access control scheme for cloud storage that is enabled by revocation. First, initialize blockchain to generate global parameters, generate complete user encryption keys and decryption keys, and perform data encryption. When the blockchain receives a user’s access request, the authorization contract determines if it is on the revocation list. If not, the key is checked. Then, it determines abnormal access and adds its identity to the revocation list, preventing further access to the database. The access control model is created by combining attribute-based encryption. After the security analysis and operational efficiency test, it can be considered that the model meets the security features, such as IND-CPA security. Regarding the time overhead of generating encryption keys, the computational overhead of this paper is the lowest, and the time required to generate encryption keys for 10 attributes is only 0.09 seconds, and for 100 attributes is only 1.62 seconds, which is better than the performance of the two attribute-based access control schemes, FIFC and AACE. The user access time overhead for 10 to 100 attributes at user encryption time is 1.38, 1.56, 1.98, 2.1, 2.53, 2.76, 3.03, 3.27, 3.66, and 3.94 seconds, respectively. The lowest decryption time consumed ensures data security and a good access experience. This study achieves fine-grained access control while protecting data privacy.

An empirical investigation of users' continuance intention to use cloud storage service for organisational uses

An empirical investigation of users' continuance intention to use cloud storage service for organisational uses

Secret Sharing as a Defense Mechanism for Ransomware in Cloud Storage Systems

Secret Sharing as a Defense Mechanism for Ransomware in Cloud Storage Systems

СОПРОВОЖДЕНИЕ ВЫВОДА ИЗ ЭКСПЛУАТАЦИИ ЯДЕРНЫХ УСТАНОВОК С ПРИМЕНЕНИЕМ ЦИФРОВЫХ ПРОДУКТОВ

The article discusses the development of a digital product for the decommissioning of nuclear and radiationhazardous facilities at the conceptual level. The need for such development is conditioned by the fulfillment of the requirements for import substitution of software in strategically important sectors of the Russian Federation, the importance of ensuring information security (data in the project is often marked «For official use») and the availability of additional functionality for specific projects that is not provided in other applications. The authors describe the features of nuclear decommissioning projects and form functional requirements for the digital product, proposing architectural patterns. In particular, the clientserver architecture with microservices was chosen for the development of this digital product. Main technologies and methodologies for the clientside application (frontend) include HTML, CSS, React, and FeatureSliced Design. JavaScript and TypeScript languages used to create the frontend of the application. To organize the backend, it is recommended to use Docker, Nest.js, and Node.js. Data storage is handled using the PostgreSQL database and S3 cloud storage. Using the example of one of the modules (Gantt Chart) of the digital product, the methods and libraries used are described in detail. Overall, the combination of these technologies ensures that the digital product is importindependent and meets all the requirements for successful development.

Внедрение передовых производственных технологий в России и оценка компаниями их влияния на рост производительности труда и эффективность производственного процесса

The urgent need for the transition of the Russian economy to a new technological structure under the influence of the current geopolitical and economic-political situation, as well as the strengthening of import substitution processes, poses the task of a qualitative breakthrough in the introduction of modern processes in industrial production and in the economy as a whole. The gen-eral process of the fourth industrial revolution involves the widespread introduction of robotic, au-tomated systems, and artificial intelligence, linked into single complexes interconnected by commu-nications and cloud storage systems. On the one hand, this process can lead to a massive release of labor resources from traditional blue-collar professions. From another point of view, it creates a large number of new professions to serve new directions and solve new problems. However, the answer to the question of the economic feasibility of introducing such technologies is not clearly positive. And here there can be quite unexpected results when the seemingly obvious benefits from the imple-mentation of certain modern solutions of the sixth technological order for specific organizations or industries are not confirmed. In our work, we use surveys of Russian companies for 2022 to assess the current stage of implementation of production and digital technologies. It has been established that the introduction of modern solutions that lead to the transition to a new technological structure is, in some cases, characterized by a negative effect and lack of improvements in labor productivity. Our observation contradicts the generally accepted idea that automation, digitalization and robotiza-tion will lead to the release of a significant number of workers. Moreover, among the key reasons hindering the implementation of advanced technologies, companies cite the lack of qualified per-sonnel and the inability to hire employees with the required level of knowledge and skills. In other words, we are observing a shortage of highly qualified employees against the backdrop of a rather contradictory effect of innovation in terms of freeing up labor. The introduction of technologies such as artificial intelligence or the Internet of things in most cases is characterized by the absence of a positive impact on the efficiency of internal processes and labor productivity. Our observations lead to the conclusion that there is no prospect of a labor surplus in the labor market due to the ongoing processes of the fourth industrial revolution according to Schwab.

Secure Federated Cloud Storage Protection Strategy Using Hybrid Heuristic Attribute-Based Encryption With Permissioned Blockchain

Secure Federated Cloud Storage Protection Strategy Using Hybrid Heuristic Attribute-Based Encryption With Permissioned Blockchain

Enhancing IoT Data Integrity and Effectiveness through hybrid Compression Method: A Step Towards Energy Efficiency

The expansion of the Internet of Things (IoT) has magnified the challenge of managing data generated by IoT devices, notably in meteorological applications like temperature and humidity monitoring. This research addresses the imperative of efficiently reducing IoT data volume while preserving data integrity and underscores the significant implications for energy consumption. Our approach involved a two-fold strategy, employing the DHT11 sensor and ESP32 microcontroller for data collection, followed by an exploration of various data compression algorithms: delta encoding, run-length encoding (RLE), variable-length integer encoding (VLI), and bit-packing. The strategic combination of RLE and delta encoding yielded an exceptional compression rate of 98%. Beyond data reduction, this methodology offers energy savings by minimizing data transmission times, evidenced by the swift 133-microsecond compression process. Furthermore, the seamless transmission of compressed IoT data to Azure Cloud not only reduced cloud storage costs but also optimized storage space, contributing to energy efficiency. This research illuminates the significance of data compression in mitigating the environmental impact of IoT technologies, fostering a greener, more energy-conscious future.

Efficient and Secure Data Deduplication in Distributed Cloud Storage: A Fault-Tolerant Model Using Active Learning for Big Data Management

As the big data era advances, more and more redundant data are being expressed in various ways. Data deduplication knowledge has never been more important than it is now for lowering redundant data storage and enhancing data quality. Connecting several data tables and identifying distinct entries that point to the same item is typically required, particularly when multi-source data deduplication is involved. Active learning minimises the amount of data that needs to be annotated and trains the classical by choosing the data pieces with the greatest evidence divergence. This approach offers special benefits when handling massive data annotations. Unfortunately, the majority of existing active learning techniques are rarely used for data deduplication jobs and only use classical entity matching. The research effort suggests a model, a distributed cloud storage method that ensures effectiveness, security, and high availability, to close this research gap. By intelligently distributing data subsets among servers, the suggested approach achieves fault tolerance while preserving redundancy for high availability. This suggested model reduces the amount of storage space and upkeep required for the data while eliminating duplicate copies. Additionally, the data is kept in a highly secure and effective manner. The suggested model's efficacy in fault tolerance, cost reduction, batch auditing, and block- and file-level deduplication is demonstrated by the experimental findings. It performs better than current systems thanks to its robust fault tolerance, minimal time complexity, and excellent deduplication capabilities.

A methodology for evaluating the effectiveness of intrusion detection system based on federated learning

The article investigates the problem of applying federated learning for building intrusion detection systems. Federated learning is a way of organizing distributed learning, in which data are not transferred to a single cloud storage, and the training of models is performed locally, the formation of a common global model is performed based on the parameters of local models. The article proposes a methodology for evaluating the performance of federated trained models, including the evaluation of the training throughput. The training throughput depends on the federated learning settings, the computational resources of the interacting devices and the network bandwidth, and obviously, the use of federated learning can significantly affect the performance of the device itself. This article presents a description of the experimental setup, and the results of a series of experiments that characterize the federated learning process with different settings.

CONTROL CLOUD DATA ACESS PRIVELAGE AND ANONYMITY WITH FULLY ANONYMOUS ATTRIBUTE BASED ENCRYPTION

Cloud computing is a revolutionary computing paradigm, which enables flexible, ondemand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute-based encryption have been proposed to secure the cloud storage. However, most work focuses on the data contents privacy and the access control, while less attention is paid to the privilege control and the identity privacy. In this paper, we present a semi-anonymous privilege control scheme AnonyControl to address not only the data privacy, but also the user identity privacy in existing access control schemes. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semianonymity. Besides, it also generalizes the file access control to the privilege control, by which privileges of all operations on the cloud data can be managed in a fine-grained manner. Subsequently, we present the AnonyControl-F, which fully prevents the identity leakage and achieve the full anonymity. Our security analysis shows that both AnonyControl and AnonyControl-F are secure under the decisional bilinear Diffie–Hellman assumption, and our performance evaluation exhibits the feasibility of our schemes.

Multi-Client Secure and Efficient DPF-Based Keyword Search for Cloud Storage

In this paper, we propose a multi-client secure and efficient keyword search scheme for cloud storage, which is built upon distributed point function (DPF). Specifically, outsourced keyword indexes are encoded by using garbled bloom filter and cuckoo filter, instead of bloom filter adopted by most of the state-of-the-art DPF-based schemes. In this way, clients can apply cuckoo hashing into DPF and utilize a segmentation method to interact with cloud servers for keyword search, and servers can obliviously aggregate DPF evaluation results to perform the search. Accordingly, the computational complexity at server side can be significantly reduced. Furthermore, the proposed scheme preserves constant downlink overheads, which is more communication-efficient for multi-keyword conjunctive search. To achieve privacy preservation and access control for multiple clients, we propose a double encryption method to encrypt outsourced indexes and correspondingly put forward an authorization algorithm from set-constrained pseudorandom functions by which fine-grained search-authorized keys can be generated, and collusion attacks among clients are addressed by integrating Wegman-Carter message authentication codes and cover-free systems. Since our scheme is designed under both semi-honest and malicious models (i.e., malicious servers may return incorrect query results), we use a simulation-based proof to formally demonstrate its security properties. Finally, we develop a proof-of-concept prototype and perform extensive experiments to show our scheme's practicality and efficiency in terms of computation, communication, and storage overheads.

Blockchain-Based Secure Distributed File Storage for Community Cloud Computing

Cloud storage is the most used option for storing and sharing files. However, cloud storage has issues like lack of transparency, privacy, security, and trust. On the other hand, local file storage is always faster and more secure than public cloud storage. Besides, a community cloud is more economical and scalable than private storage. However, due to the ad-hoc nature of community users, developing such a community cloud storage has always been a challenge due to data privacy, security, and availability concerns. Recent developments in blockchain technology and computer security are promising to resolve most of these challenges. Thus, in this research, we are presenting blockchain-based, distributed file storage for community cloud computing. This research, which focuses on local communities, allows them to securely share the excessive storage with the locals to earn something while gaining access to faster, more reliable large-scale local storage. By replacing the central authority of third-party storage vendors and migrating to distributed file storage, local communities will achieve greater security, reliability, and control over their data while reducing the risks of data failures and interruptions. Due to symmetric encryption, data privacy would be 100% assured even though they are stored in other peoples' computers. Moreover, this system uses Reed Solomon. Using the Reed Solomon algorithm, the system can retrieve files even when users holding 33.33% of the files are not present. Besides, it allows local communities to make effective use of their idle and unallocated computer resources transparently.

Mind In Vitro Platforms: Versatile, Scalable, Robust, and Open Solutions to Interfacing with Living Neurons.

Motivated by the unexplored potential of in vitro neural systems for computing and by the corresponding need of versatile, scalable interfaces for multimodal interaction, an accurate, modular, fully customizable, and portable recording/stimulation solution that can be easily fabricated, robustly operated, and broadly disseminated is presented. This approach entails a reconfigurable platform that works across multiple industry standards and that enables a complete signal chain, from neural substrates sampled through micro-electrode arrays (MEAs) to data acquisition, downstream analysis, and cloud storage. Built-in modularity supports the seamless integration of electrical/optical stimulation and fluidic interfaces. Custom MEA fabrication leverages maskless photolithography, favoring the rapid prototyping of a variety of configurations, spatial topologies, and constitutive materials. Through a dedicated analysis and management software suite, the utility and robustness of this system are demonstrated across neural cultures and applications, including embryonic stem cell-derived and primary neurons, organotypic brain slices, 3D engineered tissue mimics, concurrent calcium imaging, and long-term recording. Overall, this technology, termed "mind in vitro" to underscore the computing inspiration, provides an end-to-end solution that can be widely deployed due to its affordable (>10× cost reduction) and open-source nature, catering to the expanding needs of both conventional and unconventionalelectrophysiology.

PELATIHAN PENGGUNAAN PLATFORM MICROSOFT SEBAGAI MEDIA PEMBELAJARAN DARING

The unconducive pandemic situation has greatly affected various aspects of human life, including education. Learning that used to be done face-to-face must change to online learning. Online learning is distance learning based on internet network technology such as video conference, video call or text chat. The ability to use devices that can support the online learning process is needed by students in order to have a better learning experience. An application that can facilitate students in collaborating, communicating, and sharing learning documents is Microsoft Teams. Based on the results of the perception test conducted, it was found that the participants were familiar with several software that support online learning process. But for Microsoft platform itself, they are not familiar with and understand its use. To overcome the students' problems, a service activity was carried out to provide training on the use of Microsoft Teams application. Training activities were carried out twice and the evaluation process was carried out by giving assignments and working on pre-test and post-test questions. Various features from Microsoft are used by the students for online learning, including conversation features, meeting, cloud storage (onedrive), collaboration on the same worksheet in Microsoft Office, and so on. From the results of working on pre-test and post-test questions, it shows that there is an increase in students' skills in using the Microsoft Teams application.

IAAP: INTERDEPENDENCY ATTRIBUTE AUTHENTICATION PROTOCOL FOR IoMT SYSTEMS SECURITY ENHANCEMENT AND ANALYSIS

Internet has changed and evolved since its origin and in recent time the communication channel for the internet has developed a flexible system design to accommodate newer devices and customized services. Internet of Medical Things (IoMT) is one such blooming service for larger data processing and customization. In this paper, we propose a novel interdependency attribute protocol for authentication and enhancing security aspects of IoMT communication. The protocol is developed on remote monitoring and Message Queuing Telemetric Transport (MQTT) protocol foundation values to secure data transmission and communication via active public domain internet and cloud storage. The approach is developed on machine learning models for attribute customization and classification. The model has been validated with AWS open pipeline cloud platforms for IoMT devices customization. The IoMT device cloud server evaluation and authentication via interdependency attribute protocol has outperformed the existing security models and analysis with Machine learning tools.

Secure Cloud Storage using End-to-End Encryption

Abstract: Cloud Storage is revolutionizing how the world man- ages data by providing an incredible level of flexibility, increased capacity, and easy accessibility. Nevertheless, the technology development is associated with a number of fears related to data protection and privacy. E2EE is an end-to-end encryption concept for ensuring secure storage, transmission as well as integration of data in a cloud environment. With E2EE in place, data is encrypted right at the source and decrypted only when it reaches its intended end-points, ensuring that it remains securely hidden from eavesdroppers or other unauthorized party. As the world becomes more connected, with data housed on distant servers and traversed by broad networks, it becomes essential that one comprehends the significance of E2EE in securing cloud storage. This encryption technique does not only act as a safeguard; it is an emerging and ever-changing field that has broad implication on data protection, users’ privacy and the future of Cloudbased information management. The primary goal of this paper is to provide a thorough understanding of the evolving trends and implications of End- to-End Encryption (E2EE) in the realm of secure cloud storage. By synthesizing and analysing the latest research findings and practical implementations, we aim to shed light on how E2EE fundamentally reshapes the landscape of cloud data security.

Revocable and Traceable Undeniable Attribute-Based Encryption in Cloud-Enabled E-Health Systems.

The emerging cloud storage technology has significantly improved efficiency and productivity in the traditional electronic healthcare field. However, it has also brought about many security concerns. Ciphertext policy attribute-based encryption (CP-ABE) holds immense potential in achieving fine-grained access control, providing robust security for electronic healthcare data in the cloud. However, current CP-ABE schemes still face issues such as inflexible attribute revocation, relatively lower computational capabilities, and key management. To address these issues, this paper introduces a revocable and traceable undeniable ciphertext policy attribute-based encryption scheme (MA-RUABE). MA-RUABE not only enables fast and accurate data traceability, effectively preventing malicious user key leakage, but also includes a direct revocation feature, significantly enhancing computational efficiency. Furthermore, the introduction of a multi-permission mechanism resolves the issue of centralization of power caused by single-attribute permissions. Furthermore, a security analysis demonstrates that our system ensures resilience against chosen plaintext attacks. Experimental results demonstrate that MA-RUABE incurs lower computational overhead, effectively enhancing system performance and ensuring data-sharing security in cloud-based electronic healthcare systems.