Private Cloud Research Articles

Enhancing Cybersecurity Using Blockchain Technology Based on IoT Data Fusion

In this study, blockchain technology is used to strengthen the security of wireless sensing data in the network architecture of the Internet of Things. For many organic farms that contain crops with high unit prices, the environmental parameters of the crops grown on the farm are often regarded as confidential data. First, a wireless sensing network is used to transmit sensing data to the farm’s front-end integrated microcontroller for fusion of the sensing data. After fusion of the sensing data is completed, the processed data is transmitted to the data processing center of the entire farm for encapsulation of the blockchain algorithm. Data are sent to the cloud database for storage after they are packaged by the system. At the same time, the data in the cloud database can be managed and analyzed by remote operators. Data encapsulation based on blockchain technology is used to effectively prevent any data from being stolen or destroyed by hackers. A complete blockchain encapsulation database system is implemented in the experimental stage, and the operator can use this system to encrypt the data that have been fused at the remote end for blockchain technology. In the encryption process of this blockchain, the security of data processing can be specifically enhanced by the system. Finally, each encapsulated block of data is securely stored in a private cloud database. There is a function to check whether the data in the cloud have been tampered with. A very innovative approach to using blockchain technology is proposed in this article to enhance the security of data processing.

Recent advances in the development of data access system using remote means with raspberry pi & cloud computing

The cloud storages are becoming expensive these days and can be expected to increase more and more. The cloud storages give certain amount of the storage and then start asking the users to pay for the subscription model. Using Microprocessors and web development, it can offer you a better solution to have a personalised Private cloud storage. A private cloud storage is a storage solution that is hosted on your own network, rather than on a third-party service. This means that your data is stored in a more secure location and you have more control over it. Microprocessors can be used to create a private storage and which will be only accessible to the particular users only. Microprocessors like raspberry pi and the hard drive like SD cards, HDDs are been used to make a private cloud storage. We are installing a NAS server in the raspberry pi and create the website which is used to access the private cloud storage. NAS servers can be secured using a variety of methods, such as encryption and authentication. This helps to protect data from unauthorized access. NAS servers can be secured using a variety of methods, such as encryption and authentication. This helps to protect data from unauthorized access. The work done & presented in this paper is the result of the final year project work that has been done by the eighth sem engineering students of the college and as such there is little novelty in it and the references are being taken from various sources from the internet, the paper is being written by the students to test their writing skills in the starting of their engineering career and also to test the presentation skills during their project presentation.

СПОСОБИ З’ЄДНАННЯ З ВІРТУАЛЬНИМ СЕРВЕРОМ AWS, ЩО РОЗТАШОВАНИЙ В ПРИВАТНІЙ ПІДМЕРЕЖІ

In today's world of total digitization cyber security and safe work with data in cyberspace are the most important questions. Especially this is actual for Ukraine, where the number and power of cyberattacks has increased several times over the last year. Businesses that work with private customer data become especially vulnerable. Of course, the ideal option is to place such data on servers that don’t have Internet access. But according to the global trend of moving to the cloud, it is inevitable for private data as well. And so, there is a question of protecting private data in the cloud. To this end, cloud service providers offer services to create private subnets without Internet access. Therefore, the question of how to securely access data in such subnets become actual. One of the leader’s vendors in cloud servicing is Amazon with its Web Services. Amazon offers a Virtual Private Cloud service for setting up a virtual network. The article deals with the analysis of configuration features at the stage of creation of subnets with and without Internet access. The method of connection to a virtual server, located in a private subnet, using the Secure Shell network protocol was analyzed. However, this method has a number of disadvantages. It requires to launch an additional server and its administration. The method also has quite complex settings of the network and requires managing keys. Therefore, another method of connection to private EC2 instance was proposed. The method requires Amazon Systems Manager service, which provides secure access to data without creating additional server, is cost-effective and convenient. At the same time, all connections take place over a secure channel between the Systems Manager agent and the Amazon data center. Main setting’s features for the proposed method were considered.

Securing Cloud using Biometric Cryptographic Techniques

One of the best on-demand network access services to a significant shared pool of computer resources in the modern world is cloud computing. Many well-known economic advantages of cloud computing exist. Without having to own the underlying hardware, cloud computing enables users to take advantage of the combined benefits of three computing models for storage (Infrastructure as a Service), operating system (Platform as a Service), and software (Software as a Service) at their own premises. In spite of these financial advantages, public clouds are still not widely used, particularly by businesses. In terms of virtualized, geographically dispersed data centers, private clouds are now used by the majority of large enterprises, but they rarely serve as the primary source of resources. The main cause of this is the security risks associated with the current cloud infrastructures, which include server setup errors, software defects, power outages, hardware failures, malware, and insider threats. The goal of the research is to compare several biometric cryptography approaches for cloud security based on a variety of different criteria.

Harnessing Cloud Computing in Nigerian Educational Sector

Due to the rapid development of technology today, lots of technological advancements are on the high increase. Information storage for individual and organizations including educational sector is taking another dimension. Harnessing cloud computing in educational sector will definitely lead to the adoption of Cloud Computing in the sector. Cloud Computing is everything that could answer the challenges of ICT in educational sector and the ability to make ICT resources cheaper and it gives great flexibility for expansion, but suffered a lot of setbacks when it comes to security and privacy concerns. Since, it is where software applications, data storage and processing capacity are accessed over the internet. This paper will study the benefit of harnessing cloud computing in Educational Sector and give recommendations for adopting the new technology which can be accessed through public, private, community and hybrid cloud based technologies.

Faux Insider Hazard Investigation on Non-Public Cloud Computing by Using ADAM’s Technique

Cloud computing is a service system mechanism that businesses and organizations use to perform computerized and integrated transactions over computer networks. The service system must, of course, be”matched”with a”certain amount”of security. It is applied to” forecast the probability of cybercrime. A Cloud Service Provider (CSP) often offers cloud-based services with a basic level of security. Typically, CSPs are set up to offer their services on the open internet. Data security-focused organizations strive to shield their systems from a wide range of attackers. One of the alternatives is to construct a private cloud computing system. The issue is the potential for Man in the Cloud (MITC) assaults, which compromise and modify identities and are identified in cloud systems as phony insider threats. Based on the ISO 27032 standard research, the goal of this work is to undertake a threat analysis of MITC attack methodologies against private cloud computing services. With regards to risks to cloud services in a private cloud computing environment, it is intended that reporting and documenting the study' findings would lead to suggestions for more research and cybersecurity management procedures.

Improved secure dynamic bit standard technique for a private cloud platform to address security challenges

The numerous technologies and procedures used to safeguard private cloud infrastructure are private cloud security. Despite multitenant public cloud environments, resources are dedicated to individual companies in a private cloud. Cloud computing uses several data security mechanisms, including authentication and identification, access control, encryption, secure deletion, integrity checking, and data masking. Cloud computing relies heavily on security and traffic management. Because data must be transferred from a local computer to a remote computer when using a cloud computing service, data protection is becoming an increasingly important security problem. Research on cloud computing-based secure dynamic bit standard (CC-SDBS) technology has increased data security. This technique’s cryptographic algorithms (CA) improve cloud computing security. As a result of the private cloud security level algorithm (PCSLA), the client’s private data can be easily accessed in the cloud. For the client’s private cloud data, PCSLA provides dynamic security measures. Cloud computing applications can use the tests’ better data security in the future. The proposed algorithm’s testing results have shown a noticeable improvement in cipher size and execution time compared with other commonly used cloud computing. Numerical results revealed that CC-SDBS improved average energy consumption by 20%, average network latency by 25%, average key generation time analysis by 4.3%, and average network use by 15%.

Research on anti-attack performance of a private cloud safety computer based on the Markov-Percopy dynamic heterogeneous redundancy structure

Abstract With the increasing computing demand of train operation control systems, the application of cloud computing technology on safety computer platforms of train control system has become a research hotspot in recent years. How to improve the safety and availability of private cloud safety computers is the key problem when applying cloud computing to train operation control systems. Because the cloud computing platform is in an open network environment, it can face many security loopholes and malicious network attacks. Therefore, it is necessary to change the existing safety computer platform structure to improve the attack resistance of the private cloud safety computer platform, thereby enhancing its safety and reliability. Firstly, a private cloud safety computer platform architecture based on dynamic heterogeneous redundant (DHR) structure is proposed, and a dynamic migration mechanism for heterogeneous executives is designed. Then, a generalized stochastic Petri net (GSPN) model of a private cloud safety computer platform based on DHR is established, and its steady-state probability is solved by using its isomorphism with the continuous-time Markov model (CTMC) to analyse the impact of different system structures and executive migration mechanisms on the system's anti-attack performance. Finally, through experimental verification, the system structure proposed in this paper can improve the anti-attack capability of the private cloud safety computer platform, thereby improving its safety and reliability.

Ransomware Target: Linux. Recover Linux Data Arrays after Ransomware Attack.

The events of the previous two years have forced many businesses from many industries to fast transition to remote work mode. Most organizations were unprepared for this new method of working, and many IT professionals first risked the security of the infrastructures they supported. As a result, ransomware developers found a way to resume development of one for the second most popular operating system – Linux, which is dominant in server infrastructures. Attacks up to now have tended to concentrate more on the end user, whereas ransomware developers now target the core of the organization, allowing for greater ransom demands to recover the data. Along with these, there have been increasing reports of new cryptoviruses for Linux in recent months. This concept is not new in 2015, the first ransomware for Linux was disclosed. The paper examines the evolution of cryptoviruses in Linux and demonstrates how to utilize a private cloud to recover data arrays after a ransomware infection in Linux.

An Implementation of Divide and Conquer Clustering Technique for Improving the Interoperability in Hybrid Cloud Environment

Cloud computing provides users with pool of resources ubiquitously on demand. While the resources are provided to the users, interoperability needs to be considered. Interoperability is the ability of the cloud environment to transfer the data internally or between the data centers seamlessly. Interoperability is the least studied issue in the field of cloud computing. This paper implements hybrid cloud as a solution to interoperability. Hybrid cloud is chosen for its powerful combination of high configured and secured private clouds and fast accessible and scalable public clouds. The interoperability is then proposed to be enhanced by implementing divide and conquer algorithm of clustering in hybrid cloud.

The Process of Acquiring, Collecting, Processing and Archiving Data for the SHM System Designed to Identify Defects in Thin-Walled Structures

Abstract The article shows the results of the preparatory steps taken to create the artificial intelligence used in the automatic recognition of defects in ship thin-walled structures. The above steps are used to create a university private cloud and a computer system maintaining a dataset of vibration signal samples. In the article, a prototype of the private cloud was designed and developed, a model of the vibration sample was prepared, and a microservice was designed aimed at sharing the obtained data. The article demonstrates the results of the completed development.

AI Based Learning Model Management Framework for Private Cloud Computing

<p>Artificial Intelligence (AI) systems are computational simulations that are “educated” using knowledge and individual expert participation to replicate a decision that a professional would make provided the same data. A model tries to simulate a specific decision loop that several scientists would take if they had access to all kinds of knowledge. To convey a model, you make a model asset in AI Platform Prediction, make a variant of that model and, at that point, interface the model form to the model record put away in Cloud Storage. AI and DB information sharing are essential for cutting-edge processing for DBMS innovation. The inspirations promoting their incorporation advances incorporate the requirement for admittance to a lot of data that is shared information handling, effective administration of data as information, and astute preparation of information. Notwithstanding these inspirations, the plan for a smart information base interface (IDI) was likewise spurred by the craving to save the considerable speculation spoke to by most existing data sets. A few general ways to deal with the connectivity of AI and databases and different improvements in the area of clever information bases were already examined and announced in this paper.</p> <p> </p>

SGM: Strategic Game Model for Resisting Node Misbehaviour in IoT-Cloud Ecosystem

This paper introduces a computational strategic game model capable of mitigating the adversarial impact of node misbehaviour in large-scale Internet of Things (IoT) deployments. This security model’s central concept is to preclude the participation of misbehaving nodes during the routing process within the ad hoc environment of mobile IoT nodes. The core of the design is a simplified mathematical algorithm that can strategically compute payoff embrace moves to maximise gain. At the same time, a unique role is given to a node for restoring resources during communication or security operations. Adopting an analytical research methodology, the proposed model uses public and private cloud systems for integrating quality service delivery with secure agreements using a Global Trust Controller and core node selection controller to select an intermediate node for data propagation. The initiation of the game model is carried out by identifying mobile node role followed by choosing an optimal payoff for a normal IoT node. Finally, the model leads to an increment of gain for selecting the regular IoT node for routing. The findings of the evaluation indicate that the proposed scheme offers 36% greater accuracy, 25% less energy, 11% faster response time, and 27% lower cost than the prevalent game-based models currently used to solve security issues. The value added by the proposed study is the simplified game model which balances both security demands and communication demands.

Residents: how is headache care in general, including emergency

Introduction
 Headaches are pains in any region of the head with variable intensity, as well as its location. They can be localized or diffuse, acute or chronic, associated or not with other symptoms and have numerous causes.More than 90% of people report a history of headache during their lifetime. In addition, an overload is observed in emergency units and outpatient clinics due to inadequate initial care for a headache.
 Objective
 1) Conduct an epidemiological survey of headache care in emergency care units, analyzing the conduct of resident physicians. 2) Estimate the resolvability of headache cases in the emergency room and provide actions through continuing education if a deficiency in the care provided by professionals is demonstrated.
 Methods
 This is a descriptive observational study (cross-sectional cohort) in which we applied an online questionnaire to resident physicians who will be providing treatment in general to the patients in the city of Catanduva and neighboring cities. This is a semi-structured interview aided with self-administrated questionnaire (annexed in online in private cloud to guarantee confidentiality) which was sent via email and WhatsApp. Before joining the study, the participants filled out the informed consent form. After applying the forms, Excel spreadsheet was generated in order to process the statistical analysis. The inclusion criteria was: be a resident physician in any area of activity. The exclusion criteria: resident withdrawal at any time during the study. Number of the project in the ethics committee: 44857315.5.0000.5430
 Results
 A total of 115 residents physicians answered the questionnaire. The average age is 27,6 years and the majority are specializing in Clinical Surgery, Internal
 Medicine and Clinical Specialties. Twenty residents reported they didn't see any headache patient and the other 95 cared weekly for an average of 4,87 patients with...
 
 (To see the complet abstract, please, check out the PDF.)

Personal Health Train Architecture with Dynamic Cloud Staging

Scientific advances, especially in the healthcare domain, can be accelerated by making data available for analysis. However, in traditional data analysis systems, data need to be moved to a central processing unit that performs analyses, which may be undesirable, e.g. due to privacy regulations in case these data contain personal information. This paper discusses the Personal Health Train (PHT) approach in which data processing is brought to the (personal health) data rather than the other way around, allowing (private) data accessed to be controlled, and to observe ethical and legal concerns. This paper introduces the PHT architecture and discusses the data staging solution that allows processing to be delegated to components spawned in a private cloud environment in case the (health) organisation hosting the data has limited resources to execute the required processing. This paper shows the feasibility and suitability of the solution with a relatively simple, yet representative, case study of data analysis of Covid-19 infections, which is performed by components that are created on demand and run in the Amazon Web Services platform. This paper also shows that the performance of our solution is acceptable, and that our solution is scalable. This paper demonstrates that the PHT approach enables data analysis with controlled access, preserving privacy and complying with regulations such as GDPR, while the solution is deployed in a private cloud environment.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient’s data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Semi-Supervised Condition Monitoring and Visualization of Fused Magnesium Furnace

This paper introduces a novel practice of using image based condition classification and visualization system to augment operators in the task of monitoring the working condition of fused magnesium furnace. The system implements two functions: working condition detection and remote visually reconstruction of the furnace flame. For the problem of working condition detection, we combine the image features and the smelting electrical currents to train the classifier under semi-supervised learning framework. We also introduce a highly efficient cross-entropy based optimization method for training. For the visualization task, we propose a practical end-to-end solution which can visually simulate the dynamic furnace flame at remote monitoring consoles according to the visual feature of the monitoring video. Finally, we introduce the distributed structure of the monitoring system which consists of a private cloud, an edge server and remote monitoring consoles. The proposed solution can be applicable for various monitoring tasks in industry. Note to Practitioners—This paper introduces an image based solution to the practical problem of condition monitoring of fused magnesium furnace. The solution includes two parts: working condition detection and visualization. First, for the abnormal condition detection problem, most existing approaches use the process monitoring video as the condition predictor. It also assumes that the video data are fully labeled with condition category, which often is not available in practice. This paper suggests a novel approach combining the monitoring video and the smelting electrical currents under semi-supervised learning framework to the development of condition classifier. Experiments show that the detection accuracy is remarkably improved comparing to the approach using monitoring video only. Second, this paper presents a remote visualization solution which enables instant reconstruction of the furnace flame at monitoring consoles using only essential features of the flame. This technique is well suited for situation of real-time remote visual monitoring with very limited network bandwidth. Finally, the condition monitoring system adopts a distributed structure including a private cloud, an edge server and remote monitoring consoles. The presented techniques in this paper can be applicable for various monitoring tasks in industry.

Latency Analysis of Blockchain-Based SSI Applications

Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, many other application fields can be found in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing permissioned BC systems for SSI projects. SSI applications usually require short response times from the underlying BC network, which may vary highly depending on the application type, the used BC software, and the actual BC deployment parameters. To support the developers and users of SSI applications, we present a detailed latency analysis of a permissioned BC system built with Indy and Aries. To streamline our experiments, we developed a Python application using containerized Indy and Aries components from official Hyperledger repositories. We deployed our experimental application on multiple virtual machines in the public Google Cloud Platform and on our local, private cloud using a Docker platform with Kubernetes. We evaluated and compared their performance benchmarked by Read and Write latencies. We found that the local Indy ledger reads and writes 30–50%, and 65–85% faster than the Indy ledger running on the Google Cloud Platform, respectively.

Investigation of Fake Insider Threats on Private Cloud Computing Services

Cloud-based services are service system mechanisms used by companies or organizations in conducting computerized and integrated transactions in a computer network. A service system must of course be balanced with a level of security. This is used to anticipate cyber crimes that have the potential to occur. Cloud-based services themselves are usually offered by a Cloud Service Provider (CSP). CSPs are generally configured so that they are accessible on the public internet for their services. Companies that prioritize data security want a system that is safe from a series of cyber crimes. Private cloud computing scheme is a solution that can be implemented as an alternative. The problem that occurs is the possibility of MITC (Man in the Cloud) attacks that infiltrate and manipulate identities so that they are detected as fake insider threats on cloud systems. This thesis aims to carry out threat analysis with the Man in the Cloud attack technique on private cloud computing services based on a study of the ISO 27032 standard. Reports and documentation of the results of the analysis are expected to become recommendations for the cybersecurity investigation and management process related to threats to cloud services with private schemes cloud computing.

An Enhanced Binary Particle Swarm Optimization (E-BPSO) algorithm for service placement in hybrid cloud platforms

Hybrid cloud platforms offer an attractive solution to organizations interested in implementing integrated private and public cloud applications to meet their profitability requirements. However, this can only be achieved by utilizing available resources while speeding up execution processes. Accordingly, deploying new applications entails dedicating some of these processes to a private cloud while allocating others to the public cloud. In this context, the current work aims to minimize relevant costs and deliver effective choices for an optimal service placement solution within minimal execution time. To date, several evolutionary algorithms have been applied to solve the challenging service placement problem by dealing with complex solution spaces to provide an optimal placement with relatively short execution times. In particular, the standard BPSO algorithm has been found to display a significant disadvantage, namely getting trapped in local optima and demonstrating a noticeable lack of robustness in dealing with service placement problems. Hence, to overcome the critical shortcomings associated with the standard BPSO, an enhanced binary particle swarm optimization (E-BPSO) algorithm is proposed, comprising a modification inspired by the continuous PSO for the particle position updating equation. Our proposed E-BPSO algorithm is shown to outperform state-of-the-art approaches using a real benchmark task in terms of both cost and execution time.