Abstract

The Internet of Things (IoT) leads to the next phase of human interaction with technology. With the help of the IoT, physical objects can be given the ability to generate, receive, and seamlessly trade data with one another. The IoT includes a wide variety of applications, each of which focuses on automating a specific task and works to give inanimate objects the ability to act independently of human intervention. The currently available and upcoming IoT applications hold a great deal of promise for enhancing the level of convenience, productivity, and automation enjoyed by users. High levels of security, privacy, authentication, and the ability to recover from attacks are required for the implementation of such a world in a manner that is constantly expanding. In this light, it is necessary to make the necessary adjustments to the architecture of IoT applications to accomplish end-to-end security in IoT environments. In this article, a comprehensive review of the security-related challenges and potential sources of danger posed by IoT applications is provided. Following a discussion of security concerns, a variety of new and established technologies that are focused on achieving a high degree of trust in the applications of the IoT are covered. Machine learning, fog computing, edge computing, and blockchain are just a few of the technologies that help the IoT provide greater security.

Introduction

The Internet of Things (IoT) is one of the disruptive technologies of the 21st century that has revolutionized every business dimension across the globe. The Internet has become an essential component in daily activities such as e-commerce, e-learning, e-conferences, etc. According to [1], the number of IoT devices deployed may exceed 21.3 billion by 2022. Intelligent interfaces connect IoT devices in order to collaborate, gather, communicate, and store data. Deploying IoT devices enhances business productivity by optimizing operational procedures and maximizing the utilization of available resources. IoT brings innovation in the way things are monitored and managed remotely with the help of real-time data acquired from the sensors. With the unrestrained volumes of data generated by billions of connected devices, storage, management, and security are arduous. Security aspects such as authentication, authorization, privacy, confidentiality, availability, and integrity form the basis for information exchange in a trusted environment. Authentication is the process of identifying the genuineness of an entity. The authorization process allows only authenticated users to access the resources. The protection of privacy prevents hackers from accessing the user’s private information. Data confidentiality is an outcome of authentication and authorization. It prevents sensitive data from being reclaimed and exploited. A device’s availability confirms that IoT devices and services are resilient to attacks and are accessible all the time. Data integrity is the process of providing a tamper-proof platform for information communication, exchange, and storage.

Conclusion and future directions

In this paper, the methods that are currently available in the literature are discussed, and this paper also presents the methods that are currently available. The subsequent step is an in-depth discussion of the numerous security risks and the importance of threat modeling. The subsequent topic is a discussion of the various IoT applications’ necessary security precautions. It reviews the prior research on the numerous security issues and the solutions put forth by various researchers. The holes that were found while conducting this literature review served as inspiration for the current research work.

7.1. Research challenges
The billions of IoT devices deployed across the globe collect personal and sensitive data and exchange it with other networks using intelligent interfaces. Providing device authentication, authorization, data privacy, and security in such an un trusted environment is a challenge. Centralized trusted infrastructure cannot scale to the dynamic and ever intensifying traffic and, thus, leading to bottlenecks in the network. Distributed authentication can scale to the increasing demands of the IoT networks, but requires distributed trusted systems. The majority of the work in the IoT security domain focuses on embracing the security mechanisms of WSNs and conventional Internet-based applications. However, these mechanisms are far from being implemented in real-time scenarios due to the specific challenges of IoT, such as

Abstract

This study aims to investigate the influence of blockchain and artificial intelligence on the audit quality of firms from Turkey. Primary data from 300 respondents are collected through random sampling to attain the study’s objectives. PLS-SEM is used to investigate the relationship between exogenous and endogenous variables. Our findings show that blockchain technologies and artificial intelligence (AI) utilization in their financial system positively impact audit quality by assisting in the audit process and the detection of fraud, which also improves financial reporting. Blockchain and Artificial Intelligence in the financial system create confidence for investors, stakeholders, and legislators. Moreover, this study advocated significant implications for investors, government, firms, and policymakers. Investors can make investment decisions based on the accuracy of the financial accounts; the government and policymakers can improve the governance mechanism by using the study’s findings.

Introduction

Audits play a vital role in ensuring compliance with laws, regulations, and accounting standards. Companies are subject to various regulatory requirements and must adhere to accounting principles and reporting frameworks [1]. High-quality audits provide independent assurance that these compliance requirements are met, reducing the risk of penalties, legal consequences, or reputational damage [2]. Quality audits contribute to effective corporate governance by holding management accountable and independently assessing the company’s financial reporting [3]. By examining internal controls and risk management processes, audits help identify weaknesses and areas for improvement, enhancing organizational transparency and accountability. Audits protect the interests of various stakeholders, including employees, customers, suppliers, and lenders [4]. By assuring the accuracy and reliability of financial statements, audits help safeguard the investments, jobs, and contractual relationships associated with a company. This promotes trust and stability in the business environment.

Auditing quality is an interesting research problem and is influenced by many factors such as the blockchain, Artificial Intelligence, professional skepticism, auditor independence, audit firm size, regulatory environment, technological advancements, and the adoption of innovative audit tools. Professional skepticism plays a pivotal role in audit quality [5]. Auditor independence, ensuring auditors maintain an unbiased stance, is fundamental for objective and reliable audit outcomes [6]. The audit firm size has been identified as a factor, with larger firms often having more resources and expertise to deliver higher-quality audits [7]. The regulatory environment and oversight mechanisms significantly impact audit quality, as stringent regulations and effective oversight contribute to a more robust auditing process [8]. Technological advancements and the adoption of innovative audit tools have emerged as crucial factors shaping audit quality in the contemporary business landscape [9]. Understanding the intricate interplay of these factors is essential for advancing research and enhancing the overall quality and effectiveness of auditing practices. Therefore, these factors are widely discussed in the previous literature in different demographic contexts but lack the investigation on Artificial Intelligence (AI) and blockchain technology in relation to audit quality in the modern world.

Conclusion and recommendations

Our findings show that AI improves audit quality by providing assistance and timely fraud detection. AI’s data analysis capabilities, ability to identify complex fraud patterns, and real-time monitoring contribute to more effective and efficient audits. As AI advances, it holds tremendous potential to enhance auditing practices’ overall quality and effectiveness. This study provides specific insights into the implications of these technologies within Turkey and also other varieties of implications for investors, management, stakeholders, and policymakers. The study assessed the level of adoption and readiness of Turkish auditing firms to embrace blockchain and AI technologies. It identified the factors influencing the adoption process, including organizational culture, infrastructure, resources, and regulatory environment. Understanding the current state of technology adoption in Turkey sheds light on the potential implications for audit quality.

The implications of blockchain and AI on fraud detection and risk assessment practices in Turkish audits can be explored. The study assessed how these technologies improve the ability to detect and prevent fraud, including identifying unusual patterns, real-time monitoring of transactions, and using predictive analytics. It can also examine the impact of risk assessment methodologies and the ability to assess risks accurately. The investor can invest by seeing the audit quality of the firms within Turkey to protect their investment, and the government can make the legislation in the weakness and delayed audit without enjoying the blockchain technologies and the other factors. Future research should be conducted on the efficiency of blockchain technology that is effective in the accounting system and preparation of the financial system that makes it easier for the technologies to improve the audit quality. Some more Blockchain units should be included in further research. Due to the limitation, we included one economy in our investigation; future research should include more than one and compare each economy, the use, and the efficiency of the artificial intelligence system.

Abstract

The popularity of cloud computing, with its incredible scalability and accessibility, has already welcomed a new era of innovation. Consumers who subscribe to a cloud-based service and use the associated pay-as-you-go features have unlimited access to the applications mentioned above and technologies. In addition to lowering prices, this notion also increased the reliability and accessibility of the offerings. One of the most crucial aspects of cloud technology is the on-demand viewing of personal services, which is also one of its most significant advantages. Apps that are cloud-based are available on demand from anywhere in the world at a reduced cost. Although it causes its users pain with safety concerns, cloud computing can thrive because of its fantastic instantaneous services. There are various violations, but they all accomplish something similar, taking the systems offline. Distributed denial of service attacks are among the most harmful forms of online assault. For fast and accurate DDoS (Distributed Denial of Service, distributed denial of service) attack detection. This research introduced the DDOS attack and a method to defend against it, making the system more resistant to such attacks. In this scenario, numerous hosts are used to carrying out a distributed denial of service assault against cloud-based web pages, sending possibly millions or even trillions of packets. It uses an OS like ParrotSec to pave the way for the attack and make it possible. In the last phase, the most effective algorithms, such as Naive Bayes and Random Forest, are used for detection and mitigation. Another major topic was studying the many cyber attacks that can be launched against cloud computing.

Introduction

DDos attack is a distributed type of attack mode in which an attacker controls a large number of attack machines and sends out DoS attack instructions to the machine. In the latest Internet security report, DDoS attacks remain one of the major cybersecurity threats. The inexpensive pricing and “pay-as-you-go” focused accessibility to computational features and amenities on demand make cloud-based services a formidable competitor to the conventional IT solutions available in prior eras. The use of cloud computing is gaining popularity rapidly. Whether entirely or largely governments and companies have moved their IT infrastructures onto the cloud. Cloud-based Infrastructure offers various advantages compared to traditional, on-site conventional infrastructures. The removal of expenses associated with operation and impairment, as well as the accessibility of materials on request, are only a few of the advantages. However, there are many concerns that cloud consumers have, and the research addresses these issues. The majority of these inquiries centre on safeguarding operational concepts and information. Many security-related attacks can be prevented in conventional IT systems that do not use cloud computing. Focused cloud-based crimes are already using their innovations. Many security vulnerabilities in cloud computing are unique compared to their predecessors in non-cloud computing environments because data and business logic are stored on an external cloud server that lacks accessible oversight. The denial-of-service (DoS) assault is one technique that has been in the spotlight recently. Denial-of-service incidents are directed at the server rather than the people it supports. DoS attackers attempt to flood live servers by masquerading genuine users to overload the service’s capacity to handle incoming inquiries [1]. Cloud computing is an Internet-based service that enables users to access configurable computing resource sharing pools (including server, storage, application software, services, networks, etc.) to achieve online access to computing resources on demand. As a mixture of emerging technologies and business models, cloud computing has developed rapidly in recent years due to its advantages of super-large scale, virtualization, high reliability, good scalability and on-demand services. To overcome this issue, multiple inquiries are sent to the server simultaneously. The term “distributed denial of service,” or DDoS, refers to a variation on the classic “denial of service” that uses numerous computers to attack and impair one service at a time simultaneously.

Conclusion

The key goals of this study are to learn how to recognize and prevent attacks involving distributed denial-of-service. The first and most crucial step is determining which ports can be exploited. Nevertheless, this approach is not risk-free because susceptible ports are more likely to be exploited. Given ParrotSec’s track record for stability and performance, we decided it would be the ideal choice for our company’s computer system. Since a DDoS attack involves sending one million separate packets toward the target, starting with an on-the-internet website would be best. The targeted website was taken offline after it became clear that an assault had happened. Machine learning is constructive in this detecting process as well. Using this data, the most popular and accessible tool, “weka,” is being trained. Employing pre-processing techniques and the “discretize” filter to achieve the desired effect. Therefore, the following phase is not only quite intriguing but also rather useful for both forecasting and detecting. We employed both methods and compared the findings on the same platform, and we found that the naive Bayes method provides the most trustworthy conclusions. PCA selected 21 features from the possible 42 features, while LVQ selected only 20 features. The results suggest that LVQ based feature selection in the DT model may be more accurate than other methods in identifying attacks. As mentioned earlier, the model also outperformed the previous models in terms of accuracy, recall, specificity, and f-score. It was shown that the naive Bayes model had significantly better predictive power than the random forest model. There is a chance that a false positive rate warning will be triggered for packet transmissions within a network. Moreover, when compared to the random forest, naive Bayes produces considerably more accurate forecasts. It was demonstrated that the Naive Bayes algorithm outperformed the random forest technique to identify the false and actual rate of transmissions. The result detection is not carried out in real time. Although attacks can be detected, real-time alarm cannot be realized in the environment of high cluster security, so the feasibility of real-time monitoring under Hadoop platform should be studied continuously.

Abstract

Deep learning techniques have shown significant impact in enhancing security across various domains by leveraging artificial neural networks models. When applied to cloud computing security, deep learning offers cost-effective solutions by automating threat detection, reducing manual monitoring, and improving overall security effectiveness. Deep learning models using neural networks play crucial role in tasks like intrusion detection, malware detection, anomaly detection, and log analysis. Integration of deep learning into cloud security requires careful evaluation of existing systems, defining objectives, dataset selection and preparation, model tuning, and eventual modifications for compatibility. Furthermore, implementing deep learning techniques in cloud security entails considering factors such as computational resources, data collection and preparation costs, model development, integration efforts, and ongoing monitoring and maintenance. This paper proposes a feed-forward propagation Artificial Neural Network (ANN) model in cloud security and investigates the key steps for integrating such models into cloud security strategies. Considering that the effectiveness of the ANN model depends on factors such as training data quality, network architecture, and weight adjustment algorithms, the study utilizes a dataset from Kaggle.com for validation and demonstrates steps involved in training and evaluation of the ANN model.

Introduction

Deep learning (DL) techniques have emerged to become effective tools for enhancing security in various domains. These techniques leverage the capabilities of artificial neural networks to learn and identify patterns from vast amounts of data, enabling more robust and efficient security solutions [1][2]. When applied to cloud computing security, deep learning techniques offer several benefits having direct implications in the cost-effectiveness of the products. A great benefit of implementation of DL in security of cloud is seen in automated threat detection. DL algorithms can analyze large volumes of data, such as network traffic logs, system logs, and user behavior, to detect anomalies and potential security threats automatically [2], [3]. This together with other utilizations as a result of DL, reduces the need for manual monitoring and analysis, what leads to faster identification of security incidents, timely response, mitigation, and reduced costs in time and resources. Considering this, through automation of various security tasks, DL minimizes the risk of human error, which can lead to security breaches and associated costs.

Automated systems powered by deep learning can perform tasks consistently and accurately, enhancing overall security effectiveness [4], [5]. DL models learn patterns and relationships in data, allowing for more accurate threat detection and classification. Through detecting patterns and anomalies which could go unnoticed by traditional rulebased systems, DL implementation makes it possible for organizations to optimize resource allocation and minimize unnecessary costs.

Conclusion

The integration of deep learning techniques into cloud computing security offers numerous benefits. By analyzing large volumes of data, deep learning algorithms can detect security threats in real-time and minimize various risks. However, the implementation of deep learning in cloud security requires careful evaluation of costs and resources. Organizations struggle when having to adjust the solution to the computational resources, data acquisition and preparation costs, personnel expertise, and ongoing monitoring and maintenance.

The integration of deep learning techniques in cloud security involves few steps for a successful outcome. These include assessing the strengths and weaknesses of the existing system, identifying specific tasks that deep learning can address, selecting suitable models and algorithms, training the models with labeled data, and ensuring compatibility and integration with existing components. Continuous monitoring and feedback are also crucial stages for maintaining the efficiency and effectiveness of the integrated system.

Abstract

Cloud computing has revolutionized the way businesses and individuals manage and utilize computing resources, providing significant benefits such as scalability, flexibility, and cost-effectiveness. However, the increasing use of cloud computing to store and transmit sensitive data has raised concerns about data security. Ensuring confidentiality and data integrity while enabling effective data transmission is a significant challenge. To overcome this issue, the proposed approach integrates encryption and compression methods to enhance transmission performance in the cloud and prevent unauthorized access to confidential information. This approach applies multiple layers of robust encryption algorithms, followed by LZMA, which aims to compress data size while ensuring data security. The proposed approach is well-suited for real-time implementation and delivers high encryption quality and compression capabilities. Various performance metrics evaluate its performance, including space-saving percentage, processing time, and the NIST randomness test. It provides a substantial improvement in space-saving percentage from 58.63% to 81.8%, ensuring efficiency. The security analysis confirms that ciphertexts generated by this approach pass all NIST tests, generating a 99% confidence level regarding the randomness of the ciphertext. The proposed hybrid approach offers an effective solution for addressing the challenges of securing sensitive data while taking advantage of the benefits of cloud computing.

introduction

Cloud computing has become an increasingly popular way in the networking and computer science field. It has given small and medium-scale businesses with a cost-saving opportunity to avoid the expense of purchasing hardware resources. It gives us an equal opportunity to shine as it enables users with access to a powerful computing resource without having to invest in expensive hardware or software. Cloud computing operates on the principle of virtualization, wherein a single large machine is utilized by multiple clients with a view that they have their dedicated resources. Due to the significant benefits of managing resources in unlimited storage in the most inexpensive way, business continuity, and scalability, cloud computing has had rapid growth in the IT industry, therefore, this technology has emerged as one of the most powerful innovations that has attracted the interest of technologists globally (Sajay, 2019). Cloud computing services can be classified into three primary categories: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). SaaS provides software packages, system software, and application-related server storage networks to end users such as Gmail. PaaS is used by application developers to create applications that are hosted in the cloud such as Google App Engine. IaaS deals with hardware services, virtual machines, and network architecture infrastructure such as Microsoft Azure (Mahesh et al., 2023). The deployment models include private cloud, public cloud, community cloud and hybrid cloud. Public clouds are available to everyone while private clouds are used by organizations. Community clouds are shared by multiple organizations and hybrid clouds combine both private and public clouds (El-Booz and Attiya, 2017, Thabit et al., 2021).

Conclusion and futurework

This research utilized a multi-layered security approach with compression to secure and compress text data streams while preserving the original data sequence without any changes. This research focused on developing a secure and efficient way of storing and transferring data streams in the cloud computing environment by incorporating existing encryption and data compression techniques, including symmetric key encryption techniques Rc4, AES, and the substitution cipher Vigenère, while AES is a more advanced encryption standard. Combining these techniques provided a more robust and comprehensive solution for data security while maintaining low storage of text data through LZMA compression. The proposed approach consists of seven stages in the encoding phase (sender Side) Initial Encryption, Decomposition, Substitution, Data size treatment, Compression, Encryption2, and Uploading Encrypted-compressed data in the cloud and the inverse on the (receiver side). Experimental results proved that the proposed approach achieved faster processing time compared to performing the encryption and compression techniques as separate steps. Security analysis demonstrated that the proposed approach for generating ciphertexts passed all NIST tests with a high level of confidence (99 %) in terms of ciphertext randomness. Additionally, compression efficiency analysis demonstrates that the proposed approach produced a significantly higher range of space-saving percentages (58.63 % to 81.8 %) compared to standard techniques while also providing adequate security.

Abstract

Quantum computing is a new paradigm that will revolutionize various areas of computing, especially cloud computing. Quantum computing, still in its infancy, is a costly technology that can operate in highly isolated environments because of its rapid response to environmental factors. This makes quantum computing a challenging technology for researchers to access. These problems can be solved by integrating quantum computing into an isolated remote server, such as a cloud, and making it available to users. Furthermore, experts predict that quantum computing, with its ability to swiftly resolve complex and computationally intensive operations, will offer significant benefits in systems that process large amounts of data, like cloud computing. This article presents the vision and challenges for the quantum cloud computing paradigm that will emerge with the integration of quantum and cloud computing. Next, we present the advantages of quantum computing over classical computing applications. We analyze the effects of quantum computing on cloud systems, such as cost, security, and scalability. Besides all of these advantages, we highlight research gaps in quantum cloud computing, such as qubit stability and efficient resource allocation. This article identifies the advantages and challenges of quantum cloud computing for future research, highlighting research gaps.

Introduction

The concept of quantum first entered the literature when it was discovered by atomic physicists in the early 1900s (Heim et al., 2020). The idea that a computer could be produced using quantum mechanics was first expressed by Richard Feynman in 1981 (Kandala et al., 2017). Since it was very difficult to maintain the stability of qubits operating based on quantum superposition and quantum entanglement, the first quantum computer prototypes began to emerge in the early 2000s only with the efforts of pioneering companies such as IBM (Singh and Bhangu, 2023). Quantum computers are still in the development phase, and it is predicted that they can perform a task in as little as 200 s, which would take 10,000 years for the world’s best supercomputer (Sasaki et al., 2014). For this reason, complex and long-lasting processes such as the behavior of molecules can be carried out very quickly with quantum-based computers (von Lilienfeld et al., 2020). In addition, it is obvious that quantum computing will be one of the technologies that will shape the future as it begins to enter the military, civil, and commercial fields (Nahar et al., 2023).

In addition to the advantages they offer, quantum computers require a highly insulated environment because they are sensitive to external factors such as heat, temperature, and noise (Bernstein and Lange, 2017). For this reason, quantum computers are still an expensive and difficult-to-stabilize technology for end users. The idea of researchers being able to access quantum computer resources by integrating quantum computers with cloud computing was put forward as an extremely bright idea Ma et al. (2022). In this way, quantum computers can be placed in a highly isolated data center and provide service to the endpoints of the server. This model is called quantum cloud computing and a platform service can be offered for researchers where they can apply quantum applications and algorithms (Lou et al., 2024).

Conclusions

Quantum computing is a new paradigm that is still in its infancy but has the potential to revolutionize many scientific and technological fields in the future. Due to quantum mechanics, quantum computers operate in well-insulated environments, making them susceptible to environmental factors like heat, temperature, and noise. As a result, it is still a difficult technology for quantum researchers to reach. By integrating quantum computing into cloud platforms, a well-isolated server can provide service to the network’s edge, reducing costs. This brilliant idea may be possible with quantum cloud computing, which combines quantum computing and cloud computing. This article aims to provide guidance for researchers by highlighting the quantum cloud computing paradigm, future application areas, advantages, challenges, and research gaps.

Abstract

Cloud computing has completely changed IT (information technology) by providing IT resources as services on the internet. However, certain types of attacks, such as interdependency attacks, impede its wide adoption. With the latter, an attacker who succeeds in compromising the VM of a user can traverse the hypervisor to launch an attack on the VM(s) of other users on the same hypervisor. Unfortunately, we note a lack of secure and performant allocation policies against this problem. Existing policies focus on security but ignore other factors, including workload balance and energy consumption, which are vital for commercial cloud platforms. In this context, we propose different allocation policies for choosing the datacenter server to which we allocate a new virtual machine. These policies aim to minimize the interdependence of different users’ VMs while keeping the system performant regarding workload balance and/or power consumption. By default, our allocation policies treat all legitimate users as attackers and host their virtual machines according to their efficiency and coverage. We first design a secure and balanced solution that increases workload balance to prevent the servers from being overused. Afterward, we propose an algorithm that addresses security, power consumption, and workload balance objectives simultaneously. Based on our simulation results, our solutions perform better than existing algorithms regarding security, workload balance, and power consumption. The balanced solution reduces the chance of an attacker to zero and increases workload balance linearly. In other words, the workload balance is between [5,35] , and it utilizes slightly more hosts than existing proposals, with gains between [2,8] . Although our final proposal is less secure than previous algorithms, it performs better, so it has a good workload balance ( [5,30] ) and consumes less energy.

Introduction

Cloud computing is one of the most remarkable advances in IT in the last two decades. It offers resource consumption on demand, a flexible environment, and easy to use. These facilities make it widely adopted by the customers. However, in cloud computing, the hypervisor allows multiple virtual machines (VMs) of different users to run simultaneously on the same physical server. Ideally, each of these users’ virtual machines should operate in isolation to maintain optimal security conditions. Unfortunately, perfect logical isolation has not been achieved in practice, leaving attackers with the possibility of launching attacks such as interdependency attacks, etc. [1], [2], [3], [4], [5], [6], [7]. With the interdependency attack, a malicious user who has compromised the VM(s) of a user i can traverse the hypervisor to launch an attack on the VMs of another user j≠i on the same hypervisor.

Hence, to tackle this issue, most of the proposals tried to satisfy security and/or performance constraints by using optimization methods such as heuristic algorithms [8], [9], [10], game theory approaches [3], [11], [12], [13], the multi-objectives optimization [14], [15], [16] since the problem is NP-hard [14]. However, we note the absence of a secure and performant virtual machine allocation technique against the interdependency problem. For instance, in [16], the authors proposed a secure solution against the interdependency attack that minimizes both attacker’s efficiency and coverage, which respectively represent the probability of success of the attacker and the probability that the virtual machine of a legitimate user will be compromised. Nevertheless, this solution overlooked essential performance constraints related to minimizing power consumption and maximizing workload balance in the datacenter. These two performance constraints are very important for commercial cloud platforms. The first one motivates a provider to allocate a lot of VMs to fewer servers to reduce the cost of energy consumption and the emission of carbon dioxide (CO2). Significant energy consumption leads to high energy costs among providers. On the other hand, maximizing the workload balance spreads users’ virtual machines among the servers to prevent the hosts from being over-utilized. To accommodate these two performance constraints essential for commercial cloud platforms, we propose extending [16]. We address the interdependency problem and the interdependency attack interchangeably throughout the paper. We also refer to the performance by workload balance and power consumption.

Conclusion

In this paper, we develop the first secure and performant solution against the interdependency attack between cloud users sharing the same hypervisor. It focuses on minimizing security metrics while considering power consumption and workload balance. This approach considers all legitimate users as attackers who attempt to hack the host’s hypervisor and gain unauthorized privileges on the VMs it contains. Specifically, we define a secure allocation policy that maximizes workload balance (SALAEC-B) and a secure and performant allocation policy that simultaneously optimizes security, workload balance, and power consumption (SPALAEC). We also show that these solutions are optimal with polynomial complexities synonymous with “feasible” and “efficient”. In addition, results from the simulation show that SALAEC-B is secure and balanced, and it performs better than its counterpart in the related work, PSSF-Balanced [14]. Finally, SPALAEC is also secure against the interdependency attack while being efficient regarding workload balance and power consumption. Furthermore, our VM allocation policies prevent the negative impact that can be caused by the failure of one of the servers, unlike in PSSF-LEAST [15] and PSSF-Balanced [14]. Our allocation policies do not consider the possibility of migrating a virtual machine from one server to another. We propose an energy-aware approach with virtual machine migration as future work to deal with the high energy consumption in cloud computing and service level agreements (SLAs) violations. In addition, during allocation, our algorithms use the amount of security investment, which does not change over time (i). Our future work may investigate (i) and other simulation scenarios with many users.

Abstract

Live migration of virtual machines (VMs) is a technique that moves active VMs between different physical hosts without losing any running states. Although it is desirable for administrators that the live migration is completed as quickly as possible, the pre-copy-based live migration, widely used in modern hypervisors, does not satisfy this demand on the current trend that VMs on which running applications are performance-critical such as database management systems (DBMSes) have quite large memory. DMigrate , presented in this paper, shortens the time for live-migrating VMs with even large memory DBMSes. To quickly produce the running state of the migrating VMs on the destination, DMigrate performs regular memory transfers while simultaneously constructing the DBMS’s buffer-pool by fetching the data items from the shared storage. We prototyped DMigrate on MySQL 5.7.30, QEMU 5.1.0, and Linux 4.18.20. The experimental results show that the migration time of the prototype is up to 1.71 × and 1.71 × shorter under workloads, including sysbench and TPC-C, than the default pre-copy and post-copy schemes, respectively.

Introduction

Live migration of virtual machines (VMs) is a technique that moves active VMs between different physical hosts without losing any running states. Within a local area network (LAN), the main task of the live migration is to transfer VM memory pages to the destination while the VM disks are assumed to be stored in the shared storage. Live migration is helpful to intra-datacenter administrations including load balancing [1], [2], power saving [3], [4], and effective software upgrades [5], [6]. It is used in real-world data centers [7].

Although it is desirable for administrators that the live migration is completed as quickly as possible, the pre-copy-based live migration [8], widely used in modern hypervisors [9], [10], [11], does not satisfy this demand on the current trend that VMs on which memory-intensive applications such as database management systems (DBMSes) are running have quite large memory. For example, Amazon RDS offers VMs whose memory sizes are up to 3904 GiB [12]. Pre-copy transfers updated pages of a VM from the source to destination iteratively until the number of dirty pages is below a threshold, suspends the VM, and then passes the control to the destination after sending all dirty pages and CPU states such as register values. The migration of such VMs requires a long time due to the large number of memory page transfers. In addition, a larger VM memory size makes each iteration longer and could result in the VM spawning dirty pages that have to be transferred in the next iteration.

Conclusion

The live migration of DBMS-running VMs is non-trivial due to their tremendously large memory footprint, disturbing live migration-based administration in datacenters. This paper presented DMigrate that shortens the time for migrating DBMS-running VMs. To produce the running state of the migrating VMs on the destination, DMigrate performs regular memory transfers while simultaneously constructing the DBMS’s buffer-pool by fetching the data items from the shared storage. We prototyped DMigrate and conducted several experiments. The experimental results show that our prototype successfully shortens migration times of the pre-copy and post-copy schemes, and the prototype is effective under our synthetic migration scenarios, namely, VM eviction, batch migration, and migration in congestion.

Abstract

Cloud computing relies heavily on load balancing to distribute workloads evenly among servers, network connections, and drives. The cloud system has been assigned some load which can be underloaded, overloaded, or balanced depending on the cloud architecture and user requests. An important component of task scheduling in clouds is the load balancing of workloads that may be dependent or independent of virtual machines (VMs). To overcome these drawbacks, a novel Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards the load balancer, which contains the deep learning network called the Bi-LSTM technique. When the load is unbalanced, the VM migration will begin by sending the task details to the load balancer. The Bi-LSTM is optimized by a Genetic Expression Programming (GEP) optimizer and finally, it balances the input loads in VMs. The efficiency of the proposed LBVM has been determined using the existing techniques such as MVM, PLBVM, and VMIS in terms of evaluation metrics such as configuration latency, detection rate, accuracy etc. Experimental results shows that the proposed method reduces the Migration Time of 49%, 41.7%, and 17.8% than MVM, PLBVM, VMIS existing techniques respectively.

Introduction

Cloud services that may be accessible online are how computational hardware and software resources are packaged in cloud computing. There are three different types of cloud computing applications that have been developed: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) [ [1] , [2] , [3] ]. In order to optimize [ [4] , [5] , [6] ] streamline operations and deliver acceptable levels of speed for the consumers, handling massive data sets necessitates a number of strategies. By maintaining effective management of cloud resources, it is possible to accomplish the efficient and scalable properties of cloud computing [ 7 ]. Users might cite better utilisation of distributed resources and their application to increase throughput [ 8 ], performance [ 9 ], and problem-solving at a big scale [ 10 ] as the primary objectives of cloud computing (see Fig. 2 , Fig. 3 , Fig. 4 , Fig. 5 , Fig. 6 , Fig. 7 ).

The chances of failures that could simultaneously harm the services in cloud systems are reduced by the use of load balancing [ 11 ] and redundant mirrored databases in cluster techniques [ 12 ], which span several availability zones. The load balancer can move to another resource if one system has an outage [ 13 ]. By maximising resource availability and minimising the amount of downtime experienced by organisations during outages, load balancing techniques among the circumstances towards cloud computing help to lower expenses related to document management systems [ 14 , 15 ].

Conclusion

Load Balancing of Virtual Machine (LBVM) in Cloud Computing has been proposed in this paper. The input tasks from multiple users were collected in a single task collector and sent towards to the load balancer, there it perform the Bi-LSTM technique by using Genetic Expression Programming (GEP) optimization algorithm and the collected schedules sent to Virtual Machines (VMs) and finally it balances the input loads in VMs, When the load was unbalanced, VM migration was done. According to the results, better prediction is provided by Bi-LSTM based modelling than by traditional LSTM based models because it is based on extra data training, provides better prediction than conventional LSTM based models. In load balancing, the proposed Bi-LSTM and GEP optimization are performed, the Bi-LSTM is the optimized by GEP optimizer. The optimized cloud resources are goes to VMs which performs in cloud based and there the VMs balances the tasks. The proposed method has been evaluated in terms of MVM, PLBVM, VMIS and the proposed Load Balancing of Virtual Machine (LBVM). The proposed method reduces the Migration Time of 49%, 41.7%, 17.8% than MVM, PLBVM, VMIS existing techniques. Because cloud computing offers virtualization, scalability, infinite computational resources, and the capacity to store massive volumes of both organized and unstructured data, it is perfect for deep learning. We intend to enhance this type of load balancing for buildings that have dependent activities in the future.

Abstract

     Cloud computing platforms constructed on Trusted Computing Technology are commonly referred to as trusted cloud computing. The core component of this technology is known as Trusted Platform Module (TPM). Its specifications are given by the consortium known as Trusted Computing Group (TCG). Though TPM is an immovable module and is fixed inside any computer, yet its credentials can be shared with other devices. This paper presents the accessibility model for Virtual Machine (VM) of a distributed trust protocol which is based on TPM, through which consumer’s credentials are shared to other trusted devices. These credentials essentially describe the integrity of VM.

Introduction

     Cloud computing is a new way of outsourcing IT services as an online service. Although varying to the field of distributed, cluster or grid computing the use of the Internet in cloud computing separates it from the cluster and the grid computation. As per the terminology of cloud computing implies, the word cloud refers to the Internet that rawhides the basic enigmas of cloud computing users and conveys the seamless thinking of accepting Information Communication Technology (ICT) computation facilities from the cloud service provider (CSP). The domain of cloud computing includes three components namely Cloud Infrastructure, Cloud Provider and Cloud Consumer. Cloud computing services are used in other forms of conveyance such as Software as a Service, Platform as a Service and infrastructure as a service. According the definition of SaaS, code and databases furnished to several clients. In PaaS, software development platform is provided. IaaS allows the consumer to produce a complete and fully functional virtual machine with network capability, data storage devices, CPU or GPU processing, run time memory and almost all other resources needed for integration.

     Cloud services can be used likewise we use utilities in our daily life i.e. pay what you use. IaaS model is a fundamental layer for the cloud environment. At this layer computing resources are designed for virtualization purposes, in order to achieve widespread distribution and deployment. Due to high deployment, there is a high risk of security attacks and it is very difficult for a cloud buyer to rely on such a multitenant site. Cloud computing and IaaS security issues such as data security and privacy, trust, multiple tenants, damage, and data leaks, malicious VM etc are shown in [2] and [3]. Caring these issues it is very difficult for the consumer to rely on the infrastructure provided to the consumer. User stores data in bought machines (VM) swung on the provider facet for processing and other purposes. It is important to examine the cloud equipment for privateness and integrity before, after and at some stage in VM implementation. The literature reviewed for this motive conveys the clean impact that there may be a loss of such structures that permit the user to discover a VM controlled with integrity and privateness. In the IaaS framework all environmental safety measures are done via way of means of the provider, so the user ought to depend now no longer best on his or her infrastructure however additionally on his or her personal countermeasures

Conclusion

     Cloud computing is an internet-centric technology for outsourcing IT services such as software, platform, and infrastructure. In this research work accessibility model for the guest VM is proposed. In the proposed model integrity values of guest VM are shared and synchronized with more than one authenticated client devices to access the guest VM from everywhere and anywhere.

Abstract

Mobile cloud computing enables compute-limited mobile devices to perform real-time intensive computations such as speech recognition or object detection by leveraging powerful cloud servers. An important problem in large-scale mobile cloud computing is computational offloading, where each mobile device decides when and how much computation should be uploaded to cloud servers by considering the local processing delay and the cost of using cloud servers. In this paper, we develop a distributed threshold-based offloading algorithm where it uploads an incoming computing task to cloud servers if the number of tasks queued at the device reaches the threshold and processes it locally otherwise. The threshold is updated iteratively based on the computational load and the cost of using cloud servers. We formulate the problem as a symmetric game, and characterize the sufficient and necessary conditions for the existence and uniqueness of the Nash Equilibrium (NE) assuming exponential service times. Then, we show the convergence of our proposed distributed algorithm to the NE when the NE exists. Further, we characterize the performance gap between cost under our proposed distributed algorithm and the minimum cost in terms of Price of Anarchy (PoA) when the cost of using cloud servers is high. Finally, we perform extensive simulations to validate our theoretical findings, demonstrate the efficiency of our proposed distributed algorithm under various scenarios such as hyperexponential service times, imperfect server utilization estimation, and asynchronous threshold updates, and reveal the superior performance of threshold-based policies over their probabilistic counterpart.

Abstract

As the world grapples with the COVID-19 and its variants, multi-user collaboration by means of cloud computing is ubiquitous. How to make better use of cloud resources while preventing user privacy leakage has become particularly important. Multi-key homomorphic encryption(MKHE) can effectively deal with the privacy disclosure issue during the multi-user collaboration in the cloud computing setting. Firstly, we improve the DGHV homomorphic scheme by modifying the selection of key and the coefficients in encryption, so as to eliminate the restriction on the parity of the ciphertext modulus in the public key. On this basis, we further propose a DGHV-type MKHE scheme based on the number theory. In our scheme, an extended key is introduced for ciphertext extension, and we prove that it is efficient in performance analysis. The semantic security of our schemes is proved under the assumption of error-free approximate greatest common divisor and the difficulty of large integer factorization. Furthermore, the simulation experiments show the availability and computational efficiency of our MKHE scheme. Therefore, our scheme is suitable for the multi-user scenario in cloud environment.

Introduction

In the post-pandemic era, telecommuting has become an indispensable working mode, and emerging technologies such as cloud computing have played an important role in epidemic monitoring, prevention and control and medical assistance, showing broader application prospects and growth potential [1], [2]. According to Gartner, from 2015 to 2020, the penetration rate of global cloud computing market represented by IaaS, PaaS and SaaS increased year by year from 4.3% to 13.1%, and will rise to 15.3% in the next year. The market size is 208.3 billion dollars in 2020 and will exceed 600 billion dollars in 2025 [3].

Cloud services can eliminate the storage space constraints of personal devices and reduce local computing overhead. In practice, different companies or organizations store data in the cloud and use the cloud to share data with other members. Designing a one-to-many data sharing scheme based on attribute-based encryption(ABE) [4], [5] can achieve effective access control, but ABE is not suitable for collaborative computing between multiple members in a cloud environment. At the same time, multiple clients want to use the computing power of the cloud server to perform machine learning and data mining, collaboratively solve optimization problems and obtain optimal results. However, uploading user data to the server and training the model on the server may lead to serious user privacy disclosure. For example, in 2021, the registration information of Alibaba Cloud users was leaked, in 2022, 37 GB source code of Microsoft was leaked, and 170 million data of super star learning APP was illegally sold. The losses caused by global cybercrime more than 6 trillion in 2021, about six times as much as in 2020. With frequent privacy disclosure, the public pays more attention to privacy protection. In order to make better use of cloud services, we need a method to process data safely in the cloud environment.

Conclusion

Considering the diversity of users in the cloud computing environment, MKHE supports information storage and sharing and secure homomorphic calculation from different users. It is more suitable to solve privacy and security issues in the cloud computing, which is of great practical significance. However, the existing MKHE schemes weaken the functionality of MKHE in the expansion of ciphertext dimension and computational complexity. In this paper, we first improve the DGHV scheme [11] and propose a DGHV-type MKHE scheme. Compared with the original DGHV scheme, on the premise of ensuring security, an extended key is introduced to expand the function of the scheme. Compared with other MKHE schemes, our scheme is easier to understand in ciphertext composition and calculation form. In addition, in the experiment, the cubic form is used to reduce the public key size of the scheme to ?(? 3 ). Through experiments, we evaluate the generation time of the keys, storage capacity, expansion and homomorphic operation. However, introducing a trusted KGC when generating joint key may be difficult to meet in some practical applications. Whether to cancel the setting of KGC in joint key distribution is an important research content. We should also strive to make MKHE better applicable to the cloud environment.

Abstract

The Internet of Things (IoT) provides to the client an effective technique for communicating with the Web world through ubiquitary object enabled networks. The rapid progress in IoT connected devices creates a huge amount of data in a second from personal and industrial devices. This information should be utilized to help business and functional objectives. Thus, there is an urgent requirement for adopting cloud computing, big data, and artificial intelligence techniques to enable storage, analytics, and decision making. In this article, we focused our consideration to integrate Cloud Computing, Big data and Artificial Intelligence technique with the Internet of Things devices. Cloud computing, Big Data, Artificial Intelligence, and IoT are different techniques that are already part of our life. Their adoption and uses are expected to make them more comprehensive and make them essential components of the future Internet. The Internet of Things (IoT) is a system of interconnected gadgets, digital or mechanical machines that are given exceptional identifiers and the capacity to move information over an organization without expecting human-to-human or human-to-pc collaboration.

Introduction

With the wide-spread discovery of techniques in the current digital era, increasing physical entities are interconnected to the Internet of Things (IoT) devices. In recent years IoT technologies are applied with different techniques such as Artificial Intelligence, Big Data, and Cloud Computing. Artificial Intelligence (AI) is a technique which has the ability to compute a huge amount of task that is usually done by a human. Artificial Intelligence uses different learning techniques to facilitate automatic rules and regulations for decision making. Artificial intelligence is divided into two different modules such as learning module and predicate module [1]. The learning module is used for effective data collection, training, and data modeling. And the predicate module is used to take action on the current situation. The flow and storage of exponentially increasing data are easily managed by Artificial Intelligence (AI). The integration of cloud computing and IoT are also two different technologies that assume a vital part in our daily life. Cloud computing and the Internet of Things are merged together is expected to break both current and future internet which we called as new paradigm CloudIoT [2]. In the era of the internet which plays a fundamental role in cloud computing, it seems to be represented as a medium or the platform through which many different cloud computing services are accessible or delivered its services. If you are thinking that the internet as a virtual “space” for connecting users from over the globe, it is like a cloud, sharing information by using the internet. Cloud computing is the trending technology in the daily life of everyone which provides on-demand web services such as networking devices, data storage, servers, and applications. It provides higher flexibility and cost efficiency while users try to use cloud computing resources and applications.

Conclusion

The Internet of Things is a wide field and contains incredible and different variety of applications. The main aim of IoT is to provide a facility of exchange information and synergic performance between devices and peoples via global machine-to-machine (M2M) networks. Due to M2M network, exchange of information between the devices creates an exponential amount of data. It is impossible to manage or keep secure these generated personal or organizational data in local devices. The IoT techniques need centrally storage space for personal or industrial data. To avoid these problems, in this paper we have introduced the integration of Cloud Computing, Big Data and Artificial Intelligence techniques with the Internet of Things devices. The integration of cloud computing with IoT techniques stores the information centrally generated by the IoT devices. And the collected data which is centrally stored in cloud computing is managed, processed, and analyzed by the Big Data Techniques. To extract the high volume of IoT data in real-time, processing needs machine learning and AI algorithms.