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Cloud computing has extensively grown in recent years. A large amount of data is stored in cloud servers. To ensure confidentiality, these data is often encrypted and then stored in cloud servers. However, encryption makes range queries difficult to perform. To solve this issue, we present a scheme that facilitates fast range queries on encrypted multi-dimensional data in scenarios involving multiple users. In our scheme, we construct a tree index on encrypted multi-dimensional data, and each node is linked to a secure enhanced multi-dimensional range (MDR). To support efficient range query on the tree index, we adopt bloom filter technique. Additionally, users’ privileges are designed in a one-way calculation manner to support that different users can only perform range queries within their own privileges. Finally, we conduct extensive experiments which show the efficiency of our scheme, and also conduct a thorough analysis of its security. [ABSTRACT FROM AUTHOR]

Authentication of clients and their applications to cloud services is a major concern. Network security and the identification of hostile activities are greatly aided by intrusion detection systems (IDS). In general, optimisation strategies can be applied to improve IDS model performance. Convolutional neural networks (CNN) and other deep learning (DL) algorithms is utilised to enhance IDS’s capability to identify and categories intrusions. IDSs can identify prior attacks, adapt to changing threats, and minimise false positives by utilising these strategies. In this work, a lightweight CNN is proposed for intrusion detection in cloud environment. The main contribution of this research is to use particle swarm optimization (PSO), ametaheuristic algorithm to find the CNNs optimal parameters that comprise the number of convolutional layers, the size of the filter utilized in the convolutional procedure, the number of convolutional filters, and the batch size. Heuristicbased searches are useful for solving these kinds of problems. The experimental outcomes demonstrate that the proposed method reaches 91.70% of accuracy, 91.82% of precision, 91.99% of recall and 91.90% of F1-score. Cloud providers can gain from improved security measures by incorporating the proposed IDS paradigm into cloud settings, thereby minimizing unauthorized access and any data breaches. [ABSTRACT FROM AUTHOR]

Accelerated growth in networking and semiconductor devices led to the increase of Internet-connected devices over recent years. The applications of IoT provide pervasive admittance, scalability, and distribution of data using the cloud for the storage and processing of data. All the actors involved in the grid affect the trust. Managing security and deploying it throughout the IoT environment is required to audit and protect sensitive data. Security management involves authentication to avoid unauthorized entry and encryption at transmission. Though there are standard methodologies employed in data protection in the cloud and network layers, there is a requirement for solutions with low foot-prints suitable for constrained IoT devices. While there are existing lightweight cipher designs performing well with less memory footprint, the performance in the cloud is less compared to traditional cipher techniques. So, a balanced solution with less memory footprint and resistance against various attacks in the cloud environment is required. A lightweight cipher design is proposed to enable secure communication among devices through the cloud, which is combined with Elliptic Light (ELLI) and an Authentication Function (AF) is proposed using RC4 to ensure integrity and authentication and exchange secret key. The cipher design proposed provides increased efficiency and fixes the weakness in standard lightweight algorithms. This algorithm is simple and efficient, with 60% less memory utilization and 53% less execution time than other lightweight solutions. The stability is high in linear and differential cryptanalysis and resistive against DDoS attacks. [ABSTRACT FROM AUTHOR]

The field of computer networking is experiencing rapid growth, accompanied by the swift advancement of internet tools. As a result, people are becoming more aware of the importance of network security. One of the primary concerns in ensuring security is the authority over domains, and network owners are striving to establish a common language to exchange security information and respond quickly to emerging threats. Given the increasing prevalence of various types of attacks, network security has become a significant challenge in the realm of computing. To address this, a multi-level distributed approach incorporating vulnerability identification, dimensioning, and countermeasures based on attack graphs has been developed. Implementing reconfigurable virtual systems as countermeasures significantly improves attack detection and mitigates the impact of attacks. Password-based authentication, for instance, can be susceptible to password cracking techniques, social engineering attacks, or data breaches that expose user credentials. Similarly, ensuring privacy during data transmission through encryption helps protect data from unauthorized access, but it does not guarantee the prevention of other types of attacks such as malware infiltration or insider threats. This research explores various techniques to achieve effective attack detection. Multiple research methods have been utilized and evaluated to identify the most suitable approach for network security and attack detection in the context of cloud computing. The analysis and implementation of diverse research studies demonstrate that the based signature intrusion detection method outperforms others in terms of precision, recall, F-measure, accuracy, reliability, and time complexity. [ABSTRACT FROM AUTHOR]

The power grid, manufacturing, and industrial automation are just a few Internet of Things (IoT) settings where publish/subscribe (p/s) systems are increasingly prevalent. These systems may handle a wide range of middleware and communication protocols, ensuring compatibility. The most well-liked publish/subscribe protocol is the Message Queue Telemetry Transport Protocol (MQTT), which uses an agent to transfer information between publishers and subscribers on certain subjects. MQTT can be quickly and simply deployed for IoT settings using a popular wireless MAC layer protocol like Long Range Wide Area Network (LoRaWAN), however, this has not been properly validated. MQTT can be readily set up in cloud environments to do research experiments. To provide an MQTT-based publication design that can handle the LoRaWAN proactive steps, the authors design and provide a simulation framework in NS-3 in this study. To do this, the authors make use of the LoRaWAN library from NS-3 and include connecting it with a middleman that links to numerous publications as well as clients. The authors support many topics at the broker while enabling unicast capabilities from the broker to LoRaWAN end devices. In other words, the proposed work activates the unicast capability from the middleman to LoRaWAN peripheral devices while handling multiple topics at the mediator. To illustrate the viability of our IoT architecture and evaluate its performance at scale, the authors performed several scenarios under it. [ABSTRACT FROM AUTHOR]

Traditional big data filling algorithms often give inaccurate results due to vulnerabilities to different data types. To solve this problem, this study presents a new big data clustering algorithm powered by AI technology in a cloud environment. The study proposes an advanced Big Data clustering algorithm that leverages AI technology in a cloud environment. It optimizes clustering based on predicted strength using parallel processing. The research focuses on optimizing the clustering algorithm based on the predicted intensity through parallel processing. Experimental results demonstrate that image clustering stability is achieved when the number of clusters exceeds 4, indicating reduced sensitivity to random factors. Although it was not possible to precisely determine the optimal number of clusters, the use of an optimization algorithm showed that at four clusters the prediction intensity reached its peak, ensuring more accurate cluster identification. Through rigorous testing, the optimal number of clusters was determined to be 4. Clustering results show that visitors characterized by certain attributes show higher interest in most columns. This algorithm makes it easier to cluster incomplete large data, improves clustering speed, and improves the accuracy of filling in missing data. Compared to existing methods, this algorithm leverages AI technology in the cloud environment to optimize clustering based on prediction intensity, providing improved accuracy and efficiency during processing in big data management. [ABSTRACT FROM AUTHOR]

Resource allocation in the cloud environment for 5G applications can be explained by referring to the strategic distribution and necessary assignment of computing resources such as virtual machines, storage, and network bandwidth that meet the dynamic demands of applications and services. The framework proposed is on resource allocation in the cloud environment by BRoML for 5G applications. In the proposed BRoML model, the Backtracking Regularized model is incorporated for the effective calculation of the resources in the cloud environment. The optimization is performed for the effective computation of resources in the cloud environment through the computed resources. Using the estimated optimized values, a machine learning model can be trained and tested to classify resource allocation. In this regard, the simulation analysis is compared to BRoML with traditional schemes like SVM and RF. The result shows that BRoML has a higher resource utilization while exhibiting lower latency, higher increased throughput, and a better efficiency score overall. Machine learning techniques and optimization mechanisms give flexibility and intelligence to BRoMl in solving resource allocation issues within cloud computing. These results reinforce the view that BRoML can create a strong impact on the development process of cloud computing with its dynamic, intelligent solution in resource allocation optimization under various scenarios. [ABSTRACT FROM AUTHOR]

Nowadays, data storage in a cloud environment plays an important role in managing large scale information. However, the main problem with using a cloud environment is data security. Various encryption approaches are introduced in the existing works to provide security. However, the security issues are not resolved. This paper presented a good security enhancement with decentralized blockchain and enhanced encryption approaches. Initially, the Lamport Merkle Digital Signature Generation based blockchain approach was developed to authenticate the user data to prevent unauthorized access. This presented approach authenticates cloud users by constructing a tree, in which the leaves symbolize the hash function of sensitive user data. Then in the encryption phase, the original data is encrypted as ciphertext utilizing Optimized Elliptic curve cryptography. Here, the Collective Decision optimization approach is utilized for optimal key selection. Furthermore, the selected optimal secret key is exchanged securely utilizing the Improved Diffie-Hellman approach. The presented blockchain-based security scheme enhances data security and ensures confidential data sharing between users. The experimental results of the presented approach are examined in terms of different performance metrics. The performance analysis of the presented approach outperforms the different existing approaches. [ABSTRACT FROM AUTHOR]

Summary: The Internet is connected with everyone for sharing and monitoring digital information. However, securing network resources from malicious activities is critical for several applications. Numerous studies have recently used deep learning‐based models in detecting intrusions and received relatively robust recognition outcomes. Nevertheless, most investigations have operated old datasets, so they could not detect the most delinquent attack information. Therefore, the current research proposes the long short‐term memory (LSTM)‐based near real‐time multiclass network intrusion detection system (NIDS) utilizing complex cloud CSE‐CICIDSS2018 datasets to secure and detect the network anomalous. The proposed strategy utilizes a random forest algorithm for dimensionality reduction and feature selection. In addition, the selected best suitable features were used in a deep learning‐based LSTM model developed for detecting network intrusions. The experimental outcomes reveal that the presented LSTM model obtained 99.66% testing accuracy with 0.12% loss. Thus, the suggested approach can detect network intrusions with the highest precision and lowest rate over the earlier designs. [ABSTRACT FROM AUTHOR]

Recently, digital images are widely used in several applications through advanced wearable devices and networks. Despite several benefits of digital images, such as easy distribution, storage, and reproduction, it is difficult to prevent the issue of identity theft, privacy leakage, and ownership conflicts. To solve the above issues, dimensionality reduction-based robust watermarking for color images, supported with suitable encryption, is presented in this article. In this method, principal component analysis (PCA)-based dimensionality reduction is applied to the color carrier image to produce three principal components. The third component with minimum energy is then concealed with a mark image using non-sub-sampled shearlet transform (NSST)-singular value decomposition (SVD)-based watermarking to resolve the ownership issues, if any. In addition, encryption of marked image is done to provide better security and authenticity. The final encrypted image is then stored in a cloud environment for better accessibility. Finally, an experimental and comparative evaluation of the proposed framework justifies its versatility, robustness, and imperceptibility with the best improvement of 47.49 %. [ABSTRACT FROM AUTHOR]

The present study uses the benchmark UNSW-NB15 datasets to detect network anomalies using the proposed deep learning-based Convolutional Neural Network (CNN) model. Several studies have already worked on network anomaly detection with some limits. However, earlier research has used old datasets and obtained limited accuracy. In addition, previous studies could not detect the latest malicious activities. Therefore, in the present study, firstly, we implemented the machine learning-based random forest method to diminish the dimensionality of the dataset and pick the most notable features. In this case, the random forest method has set only fifteen features from the forty-one and reduced the complexity of the data. Subsequently, the CNN model was trained and tested on the UNSW-NB15 dataset with 99.00% testing accuracy. The performance of the offered CNN model was assessed using precision, recall and the f1-values. The present study's outcome can be used in real-time malicious activity detection. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Secure and powerful Industrial Internet of Things (IIoT) statistics dealing with on cloud infrastructures is vital as commercial gadgets grow to be greater networked. IIoT systems accommodated in the cloud should shield personal statistics, make sure uninterrupted operations, use information insights to make decisions, and reduce electricity consumption. Several industries have been transformed by way of IIoT programs, which depend closely on cloud infrastructure for statistics processing and garage. Energy performance and the safety of sensitive business statistics are predominant issues. A few of the problems that need addressing are secure data transmission, invasion of privacy, and data breaches. It is not a simple task to optimize power efficiency without compromising actual-time records processing. The Optimized Dynamic Clustering and Energy-Efficient System (ODC-EES) is a unique approach for cloud-based IIoT information control and employer that uses stepped forward adaptive clustering strategies. Strengthening facts security whilst streamlining strength use, the recommended method blends present day encryption protocols, access controls, and power-aware useful resource allocation. This method promotes sustainable electricity practices even as making sure adaptability to the ever-converting IIoT information. Manufacturing, strength, logistics, and healthcare are the various few of the numerous commercial sectors that might advantage from ODC-EES. The counselled approach seeks to enhance the dependability and performance of manufacturing strategies through making IIoT information more stable and the use of less strength. For the motive to demonstrate the system's efficacy in enhancing statistics protection, optimizing energy usage, and making sure the fresh operation of IIoT programs in cloud environments, these simulations will evaluate its overall performance below numerous situations.

Cloud computing has revolutionized the way businesses operate by offering accuracy in Normalized Mutual Information (NMI). However, with the growing adoption of cloud services, ensuring the accuracy and validation of common processes through machine learning and clustering of these common concepts as well as of the processes generated by cloud forensics experts' data in cloud environments has become a paramount concern. The current paper proposes an innovative approach to enhance the data collection procedure in cloud environments by applying a Cloud Forensic Meta-Model (CFMM) and integrating it with machine learning techniques to improve the cloud forensic data. Through this approach, consistency and compatibility across different cloud environments in terms of accuracy are ensured. This research contributes to the ongoing efforts to validate the clustering process for data collection in cloud computing environments and advance the field of cloud forensics for standardizing the representation of cloud forensic data, certifying NMI and accuracy across different cloud environments. [ABSTRACT FROM AUTHOR]

The intersection of education and technology has witnessed a significant transformation in recent years, with a particular focus on optimizing remote learning experiences. Maintaining network resources for brass music education is crucial to ensure a seamless and effective learning experience. Hence, this paper proposed SDNcOIT (Software-Defined Networking cloud Optimal Information Technology) model in the context of brass music education, a discipline that demands real-time audio and video interactions. The proposed SDNcOIT model uses the cloud environment integrated SDN architecture for the evaluation of brass music education. The constru cted network comprises Software-Defined Networking, cloud technology, and a rule-based model, the SDNcOIT system presents a compelling case for enhancing the delivery of music education to students worldwide. The SDNcOIT model implemented the optimization model within the SDN environment for the analysis of the evaluation of the user experience in the cloud environment. The proposed SDNcOIT model uses the gradient descent optimization model for the computation of the brass music in the cloud environment. Through the implementation of the optimization process within the SDNcOIT the features are optimized in the cloud with SDN architecture for the analysis of user experience. The findings from this study reveal substantial performance improvements over time, exemplified by reduced latency, increased throughput, minimized packet loss, and elevated user satisfaction. These improvements are pivotal for a discipline where the quality of audio and video content is paramount. The scalability of the cloud infrastructure ensures that the system can accommodate varying dataset sizes, adapting seamlessly to the dynamic requirements of a growing brass music program. Furthermore, the rule-based model's growing adherence to network behavior results in an exceptionally efficient and well-optimized network, aligning harmoniously with the educational objectives of brass music instruction. [ABSTRACT FROM AUTHOR]

An effective security model with low computational complexity, minimal quality-compromised, and improved security robustness is essential due to the rapid expansion of multimedia data communication through different cloud services. An image steganography model has been proposed for the security of multimedia data in an unreliable cloud environment. This study's main goal is to provide efficient steganography with barely hidden information. A secure data communication model has been developed over a cloud environment using the Adaptive Artificial Bee Colony Algorithm and the Optimum Pixel Adjustment Algorithm Based Image Steganography Method. An adaptive ABC (artificial bee colony) technique is applied to select the optimal pixel positions in the cover image because the goal of this investigation is to increase PSNR. The Optimal Pixel Adjustment approach is used to minimise embedding errors while maintaining the stego image's appearance identical to the cover image after embedding. The MATLAB platform is used to implement the proposed method. The results show that the proposed AABC-based OPA is more efficient across all measures investigated during the embedding and extraction processes. [ABSTRACT FROM AUTHOR]

With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.

Cloud computing has become a widespread technology that delivers a broad range of services across various industries globally. One of the crucial features of cloud infrastructure is virtual machine (VM) migration, which plays a pivotal role in resource allocation flexibility and reducing energy consumption, but it also provides convenience for the fast propagation of malware. To tackle the challenge of curtailing the proliferation of malware in the cloud, this paper proposes an effective strategy based on optimal dynamic immunization using a controlled dynamical model. The objective of the research is to identify the most efficient way of dynamically immunizing the cloud to minimize the spread of malware. To achieve this, we define the control strategy and loss and give the corresponding optimal control problem. The optimal control analysis of the controlled dynamical model is examined theoretically and experimentally. Finally, the theoretical and experimental results both demonstrate that the optimal strategy can minimize the incidence of infections at a reasonable loss.

Because of its on-demand servicing and scalability features in cloud computing, security and confidentiality have converted to key concerns. Maintaining transaction information on third-party servers carries significant dangers so that malicious individuals trying for illegal access to information data security architecture. This research proposes a security-aware information transfer in the cloud-based on the blowfish algorithm (BFA) to address the issue. The user is verified initially with the identification and separate the imported data using pattern matching technique. Further, BFA is utilised to encrypt and save the data in cloud. This can safeguard the data and streamline the proof so that client cannot retrieve the information without identification which makes the environment secure. The suggested approach's performance is evaluated using several metrics, including encryption time, decryption time, memory utilisation, and runtime. Compared to the existing methodology, the investigational findings clearly show that the method takes the least time to data encryption. [ABSTRACT FROM AUTHOR]

Purpose: The present research aims to examine and offer a model of a cloud-based knowledge-sharing process among the faculty members of Islamic Azad University (IAU).Methods: This research is applied in terms of purpose, and in terms of type of research, it is mixed exploratory, and in terms of research method, it is research-based on grounded theory. For the purpose of the research, all 2800 faculty members of Islamic Azad University (IAU) in the country were studied. To determine the sample size (397 people), a multi-stage sampling method and Cochran's sample size formula were used. In this research, structured interviews and electronic questionnaires were used as research tools in two qualitative and qualitative stages. In order to achieve the objectives of the research, the interview with the experts continued until the theoretical adequacy. The pattern of knowledge-sharing behavior was extracted from the data of the qualitative phase of the research. Also, the research employed structural equations (Partial Least Square Model) to analyze the research data, using smartPLS software.Findings: The research findings indicated that there were six effective components connected with the exploratory pattern of knowledge-sharing behavior, which are given below in order of priority: 1. Principal Component; the pivotal component with the factor of organizational maturity level, 2. Causal Conditions; including the management and organizational factors, 3. Strategies; including the factors affecting the development of job security and the improvement of the level of trust across organizations and organizational communication network, 4. Background Conditions; including organizational culture and technological infrastructures 5. Intervening Conditions, including the enhancement of workforce skills, and 6. Consequences. These include the factors affecting the efficiency of human resources and the development of occupational engagement.Similarly, the research results indicated that both the management factor and the organizational factors, as the dual elements of the component causal conditions, influenced the organizational maturity development process. This is because the T-statistic value given the effectiveness of the management factors and the path coefficient equaled 3.666 and 0.21, respectively, which is indicative of the direct positive effects of the management factors on organizational maturity. The organizational factors affected the maturity process directly and positively since the T-statistic value was 6.334 going beyond the absolute value of 1.96, and the path coefficient was 0.327.Conclusions: Organizations can pave the way for the development of organizational maturity if they are in favorable conditions in terms of support from managers and the coordination between cloud computing and knowledge-sharing systems. The implementation of such systems across organizations will result in improved performance of the faculty members and an increased competitive advantage.

Internet of things (IoT) and cloud computing (CC) becomes widespread in different application domains such as business, e-commerce, healthcare, etc. The recent developments of IoT technology have led to an increase in large amounts of data from various sources. In IoT enabled cloud environment, load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization. The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics. In this view, this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling (C3SOA-LS) technique for IoT enabled cloud environment. The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished. Besides, the presented C3SOA-LS model involves the design of circle chaotic mapping (CCM) with the traditional chameleon swarm optimization (CSO) algorithm for improving the exploration process, shows the novelty of the work. The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan. The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches. [ABSTRACT FROM AUTHOR]

Simulation of a Big data streams data center (BDSDC) reengineered for efficient dumpsite datastream offloading. A use case municipal waste management aggregation data center network (DCN) and cloud-driven micro-services orchestration at the edge with low latency translation into the cloud environments. Discrete-Event Modelling and Simulation Methodology (DEMSM) was adopted. Using the experimental test data gathered from the experimental testbed (UNN DCN), a simulation study was carried out in Riverbed Modeller while allowing for result comparison with the trace file of the typical traditional DCN. It was discovered that BDMSC performed much better that the traditional DCN and addressed majority of the challenges. The results of the proposed BDSDC system considered BDSCA Optimization, and non-BDSCA Optimization use-cases. Second, the proposed BDSA was then compared with Bayesian and MapReduce algorithms. With BDSCA Optimization, 47.37% data stream workload is provisioned which is very useful in deterministic traffic workloads. This is in contrast with the best-efforts scheme that yielded 52.63%. In terms of throughput, proposed BDSA offered 52.63% throughput cycles while Bayesian and MapReduce gave 36.84% and 10.53% each. Considering network latency, the proposed BDSCA latency optimization is shown to be very attractive at 27.77%. This is certainly better than Bayesian (55.56%) and MapReduce (16.67%). In terms of resource utilization, at peak traffic, all the algorithms had similar trend pattern even at the steady and relaxed states. At a closer experimental control and monitoring, the resource utilization, MapReduce Apriori, Bayesian and the proposed BDSCA offered 37.55%, 25.03% and 37.42% respectively. Finally, this is better than reactive DCell and BCube integration cloud domains. [ABSTRACT FROM AUTHOR]

Cloud computing is revolutionizing Customer Internet computing and the IT industry by integrating with mobile environments through remote access technologies. This growth involves using smartphones and sensors as data collection nodes interacting with the cloud. However, widespread adoption faces a hurdle in data security concerns, as users are hesitant about entrusting sensitive data to public clouds operated by external providers. To tackle barriers in cloud computing, we urgently require innovative and secure management architectures. This paper introduces a comprehensive framework addressing user concerns, focusing on secure data sharing in the cloud. The framework covers aspects like transport, aggregation, usage, and destruction of sensitive data, emphasizing the semi-trusted nature of the cloud to boost user confidence and address security concerns. One key aspect of the proposed framework is the integration of the Kerberos protocol over the network. This protocol plays a pivotal role in establishing secure and authenticated communication channels, ensuring the confidentiality and integrity of the data being transferred within the cloud infrastructure. Additionally, the paper introduces a user process protection method based on a virtual machine monitor. This method contributes to enhancing overall system security by providing a layer of isolation and protection for user processes operating in the cloud environment. The combined integration of the Kerberos protocol and the virtual machine monitor-based user process protection method forms a cohesive approach towards realizing robust system functionalities. By offering a secure foundation for data management in the cloud, this framework addresses the current limitations and instills a sense of trust among users, fostering a more widespread and confident adoption of cloud computing techniques. [ABSTRACT FROM AUTHOR]

One of the most common causes of blackouts is unexpected failures at power system transformer levels. The purpose of this project is to create a low-cost Internet of things (IoT)-based monitoring system for power grid transformers in order to investigate their working status in real-time. Our monitoring system's key functions are the gathering and display of many metrics measured at the transformer level (temperature, humidity, oil level, voltage, vibration, and pressure). The data will be collected using various sensors connected to a microcontroller with an embedded Wi-Fi module (DOIT Esp32 DevKit v1), and then supplied to a cloud environment interface with a full display of all the ongoing changes. This technology will provide the power grid maintenance center with a clear image of the transformers' health, allowing them to intervene at the right time to prevent system breakdown. The method described above would considerably improve the efficiency of a power transformer in a smart grid system by detecting abnormalities before they become critical. [ABSTRACT FROM AUTHOR]

In today’s world, services are improved and advanced in every field of life. Especially in the health sector, information technology (IT) plays a vigorous role in electronic health (e-health). To achieve benefits from e-health, its cloud-based implementation is necessary. With this environment’s multiple benefits, privacy and security loopholes exist. As the number of users grows, the Electronic Healthcare System’s (EHS) response time becomes slower. This study presented a trust mechanism for access control (AC) known as role-based access control (RBAC) to address this issue. This method observes the user’s behavior and assigns roles based on it. The AC module has been implemented using SQL Server, and an administrator develops controls for users with roles and access to multiple EHS modules. To validate the user’s trust value, A.net-based framework has been introduced. The framework of e-health proposed in this research ensures that users can protect their data from intruders and other security threats.

In cloud environments, optimization of resource utilizations is one among the predominant challenges. The two sub-research topics are cloud resource prediction and allocation. A few contributions to virtual machine (VM) placement techniques have been identified in the literature. In order to efficiently put up the virtual machine (VM) on the physical machine (PM), a Self Adaptive Immigrants with Genetic Algorithm (SAI-GA) is presented in this study. Based on CPU and memory usage, the proposed technique would forecast the best PM for each VM. The algorithm will adjust itself with the appropriate immigrant based on the history of past VM placement to find the best VM placement. In this paper, the VM live dataset from the CSAP lab at SNU in Korea has been used. For the purpose of demonstrating the significance of the findings, a number of non-parametric tests were used to evaluate how well the proposed SAI-GA performed. The outcomes demonstrate that the suggested approach makes a considerable contribution to the placement of VMs in cloud environments.