The rapid advancement of cloud technology has resulted in the emergence of many cloud service providers. Microsoft Azure is one among them to provide a flexible cloud computing platform that can scale business to exceptional heights. It offers extensive cloud services and is compatible with a wide range of developer tools, databases, and operating systems. In this paper, a detailed analysis of Microsoft Azure in the cloud computing era is performed. For this reason, the three significant Azure services, namely, the Azure AI (Artificial Intelligence) and Machine Learning (ML) Service, Azure Analytics Service and Internet of Things (IoT) are investigated. The paper briefs on the Azure Cognitive Search and Face Service under AI and ML service and explores this service s architecture and security measures. The proposed study also surveys the Data Lake and Data factory Services under Azure Analytics Service. Subsequently, an overview of Azure IoT service, mainly IoT Hub and IoT Central, is discussed. Along with Microsoft Azure, other providers in the market are Google Compute Engine and Amazon Web Service. The paper compares and contrasts each cloud service provider based on their computing capability.

The enhancement of big data security in cloud computing has become inevitable dues to factors such as the volume, velocity, veracity, Value, and velocity of the big data. These enhancements of big data and cloud technologies have computing enabled a wide range of vulnerabilities in applications in organizational business environments leading to various attacks such as denial-of-service attacks, injection attacks, and Phishing among others. Deploying big data in cloud computing environments is a rapidly growing technology that significantly impacts organizations and provides benefits such as demand-driven access to computational services, a distorted version of infinite computing capacity, and assistance with demand-driven scaling up, scaling down, and scaling out. To secure cloud computing for big data processing, a variety of encryption techniques such as RSA, and AES can be applied. However, there are several vulnerabilities during processing. The paper aims to explore the enhancement of big data security in cloud computing using the RSA algorithm to improve the deployment and processing of the variety, volume, veracity, velocity and value of the data utilizing RSA encryptions. The novelty contribution of the paper is threefold: First, explore the current challenges and vulnerabilities in securing big data in cloud computing and how the RSA algorithm can be used to address them. Secondly, we implement the RSA algorithm in a cloud computing environment using the AWS cloud platform to secure big data to improve the performance and scalability of the RSA algorithm for big data security in cloud computing. We compare the RSA algorithm to other cryptographic algorithms in terms of its ability to enhance big data security in cloud computing. Finally, we recommend control mechanisms to improve security in the cloud platform. The results show that the RSA algorithm can be used to improve Cloud Security in a network environment.

The surveillance factor impacting the Internet-of-Things (IoT) conceptual framework has recently received significant attention from the research community. To do this, a number of surveys covering a variety of IoT-centric topics, such as intrusion detection systems, threat modeling, as well as emerging technologies, were suggested. Stability is not a problem that can be handled separately. Each layer of the IoT solutions must be designed and built with security in mind. IoT security goes beyond safeguarding the network as well as data to include attacks that could be directed at human health or even life. We discuss the IoT s security challenges in this study. We start by going over some fundamental security ideas and IoT security requirements. Following that, we look at IoT market statistics and IoT security statistics to see where it is all headed and how to make your situation better by implementing appropriate security measures.

Cloud computing has turned into an important technology of our time. It has drawn attention due to its, availability, dynamicity, elasticity and pay as per use pricing mechanism this made multiple organizations to shift onto the cloud platform. It leverages the cloud to reduce administrative and backup overhead. Cloud computing offers a lot of versatility. Quantum technology, on the other hand, advances at a breakneck pace. Experts anticipate a positive outcome and predict that within the next decade, powerful quantum computers will be available. This has and will have a substantial impact on various sciences streams such as cryptography, medical research, and much more. Sourcing applications for business and informational data to the cloud, presents privacy and security concerns, which have become crucial in cloud installation and services adoption. To address the current security weaknesses, researchers and impacted organizations have offered several security techniques in the literature. The literature also gives a thorough examination of cloud computing security and privacy concerns.

IoT shares data with other things, such as applications, networked devices, or industrial equipment. With a large-scale complex architecture de-sign composed of numerous ‘things’, the scalability and reliability of various models stand out. When these advantages are vulnerable to security, constant problems occur continuously. Since IoT devices are provided with services closely to users, it can be seen that there are many users with various hacking methods and environments vulnerable to hacking.

Internet of Things (IoT) is encroaching in every aspect of our lives. The exponential increase in connected devices has massively increased the attack surface in IoT. The unprotected IoT devices are not only the target for attackers but also used as attack generating elements. The Distributed Denial of Service (DDoS) attacks generated using the geographically distributed unprotected IoT devices as botnet pose a serious threat to IoT. The large-scale DDoS attacks may arise through multiple low-rate DDoS attacks from geographically distributed, compromised IoT devices. This kind of DDoS attacks are difficult to detect with the existing security mechanisms because of the large-scale distributed nature of IoT. The proposed method provides solution to this problem using Fog computing containing fog nodes which are closer to edge IoT devices. The distributed fog nodes detects the low-rate DDoS attacks from IoT devices before it leads to largescale DDoS attack. The effectiveness analysis of the proposed method proves that the real time detection is practical. The experimental results depicts that the lowrate DDoS attacks are detected at faster rate in fog nodes, hence the large-scale DDoS attacks are detected at early stage to protect from massive attack.

Different contemporary organisations are using cloud computing application in business operation activities to gain competitive advantages over other competitors. It also helps in promoting flexibility of the business operation activities. Cloud computing involves delivery of different computer resources to data centres over the internet services. Different kinds of delivered computer resources include data storage, servers, database, analytics, software, networking, and other types of data applications etc. In this present era of data breaches, cloud computing ensures security protocols to protect different kinds of sensitive transaction data and confidential information. Use of cloud computing ensures that a third party individual does not tamper the data. Use of cloud computing also provides different kinds of competitive advantages to the organisations. Cloud computing also helps in providing efficiency and a platform for innovation for the contemporary organisations. Theoretical frameworks are usedin the literature review section to determine the important roles of cloud computing in effective data and security management in the organisations. It is also justified in the research work that qualitative methodology is suitable for the researcher to meet the developed research objectives. A secondary data analysis approach has been considered by the researcher in this study to carry out the investigation and meet the developed objectives. From the findings, few challenges associated with the cloud computing system have been identified. Proper recommendations are suggested at the end of the study to help future researchers in overcoming the identified associated challenges.

With billions of devices already connected to the network s edge, the Internet of Things (IoT) is shaping the future of pervasive computing. Nonetheless, IoT applications still cannot escape the need for the computing resources available at the fog layer. This becomes challenging since the fog nodes are not necessarily secure nor reliable, which widens even further the IoT threat surface. Moreover, the security risk appetite of heterogeneous IoT applications in different domains or deploy-ment contexts should not be assessed similarly. To respond to this challenge, this paper proposes a new approach to optimize the allocation of secure and reliable fog computing resources among IoT applications with varying security risk level. First, the security and reliability levels of fog nodes are quantitatively evaluated, and a security risk assessment methodology is defined for IoT services. Then, an online, incentive-compatible mechanism is designed to allocate secure fog resources to high-risk IoT offloading requests. Compared to the offline Vickrey auction, the proposed mechanism is computationally efficient and yields an acceptable approximation of the social welfare of IoT devices, allowing to attenuate security risk within the edge network.

As a result of this new computer design, edge computing can process data rapidly and effectively near to the source, avoiding network resource and latency constraints. By shifting computing power to the network edge, edge computing decreases the load on cloud services centers while also reducing the time required for users to input data. Edge computing advantages for data-intensive services, in particular, could be obscured if access latency becomes a bottleneck. Edge computing raises a number of challenges, such as security concerns, data incompleteness, and a hefty up-front and ongoing expense. There is now a shift in the worldwide mobile communications sector toward 5G technology. This unprecedented attention to edge computing has come about because 5G is one of the primary entry technologies for large-scale deployment. Edge computing privacy has been a major concern since the technology’s inception, limiting its adoption and advancement. As the capabilities of edge computing have evolved, so have the security issues that have arisen as a result of these developments, as well as the increasing public demand for privacy protection. The lack of trust amongst IoT devices is exacerbated by the inherent security concerns and assaults that plague IoT edge devices. A cognitive trust management system is proposed to reduce this malicious activity by maintaining the confidence of an appliance \& managing the service level belief \& Quality of Service (QoS). Improved packet delivery ratio and jitter in cognitive trust management systems based on QoS parameters show promise for spotting potentially harmful edge nodes in computing networks at the edge.

The big data platform based on cloud computing realizes the storage, analysis and processing of massive data, and provides users with more efficient, accurate and intelligent Internet services. Combined with the characteristics of college teaching resource sharing platform based on cloud computing mode, the multi-faceted security defense strategy of the platform is studied from security management, security inspection and technical means. In the detection module, the optimization of the support vector machine is realized, the detection period is determined, the DDoS data traffic characteristics are extracted, and the source ID blacklist is established; the triggering of the defense mechanism in the defense module, the construction of the forwarder forwarding queue and the forwarder forwarding capability are realized. Reallocation.

The computing capability of the embedded systems and bandwidth of the home network increase rapidly due to the rapid development of information and communication technologies. Many home appliances such as TVs, refrigerators, or air conditioners are now connected to the internet, then, the controlling firmware modules are automatically updatable via the network. TR-069 is a widely adopted standard for automatic appliance management and firmware update. Maintaining a TR069 network usually involves the design and deployment of the overall security and trust infrastructure, the update file repository and the update audit mechanisms. Thus, maintaining a dedicated TR-069 network is a heavy burden for the vendors of home appliances. Blockchain is an emerging technology that provides a secure and trust infrastructure based on distributed consensus. This paper reports the results of our initial attempt to design a prototype of a multitenant TR-069 platform based on the blockchain. The core idea is to reify each automatic deployment task as a smart contract instance whose transactions are recorded in the append-only distributed ledger and verified by the peers. Also, the overall design should be transparent to the original TR069 entities. We have built a prototype based on the proposed architecture to verify the feasibility in three key scenarios. The experimental results show that the proposed approach is feasible and is able to scale linearly in proportion to the number of managed devices.

The computing capability of the embedded systems and bandwidth of the home network increase rapidly due to the rapid development of information and communication technologies. Many home appliances such as TVs, refrigerators, or air conditioners are now connected to the internet, then, the controlling firmware modules are automatically updatable via the network. TR-069 is a widely adopted standard for automatic appliance management and firmware update. Maintaining a TR069 network usually involves the design and deployment of the overall security and trust infrastructure, the update file repository and the update audit mechanisms. Thus, maintaining a dedicated TR-069 network is a heavy burden for the vendors of home appliances. Blockchain is an emerging technology that provides a secure and trust infrastructure based on distributed consensus. This paper reports the results of our initial attempt to design a prototype of a multitenant TR-069 platform based on the blockchain. The core idea is to reify each automatic deployment task as a smart contract instance whose transactions are recorded in the append-only distributed ledger and verified by the peers. Also, the overall design should be transparent to the original TR069 entities. We have built a prototype based on the proposed architecture to verify the feasibility in three key scenarios. The experimental results show that the proposed approach is feasible and is able to scale linearly in proportion to the number of managed devices.

Internet-scale Computing Security - With the rapid growth of the number of global network entities and interconnections, the security risks of network relationships are constantly accumulating. As the basis of network interconnection and communication, Internet routing is facing severe challenges such as insufficient online monitoring capability of large-scale routing events and lack of effective and credible verification mechanism. Major global routing security events emerge one after another, causing extensive and far-reaching impacts. To solve these problems, China Telecom studied the BGP (border gateway protocol) SDN (software defined network) controller technology to monitor the interconnection routing, constructed the global routing information database trust source integrating multi-dimensional information and developed the function of the protocol level based real-time monitoring system of Internet routing security events. Through these means, it realizes the second-level online monitoring capability of large-scale IP network Internet service routing events, forms the minute-level route leakage interception and route hijacking blocking solutions, and achieves intelligent protection capability of Internet routing security.

Internet-scale Computing Security - Wireless Sensor networks can be composed of smart buildings, smart homes, smart grids, and smart mobility, and they can even interconnect all these fields into a large-scale smart city network. Software-Defined Networking is an ideal technology to realize Internet-of-Things (IoT) Network and WSN network requirements and to efficiently enhance the security of these networks. Software defines Networking (SDN) is used to support IoT and WSN related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. This work is a study of different security mechanisms available in SDN for IoT and WSN network secure communication. This work also formulates the problems when existing methods are implemented with different networks parameters.

Internet-scale Computing Security - Cloud computing forms the backbone of the era of automation and the Internet of Things (IoT). It offers computing and storage-based services on consumption-based pricing. Large-scale datacenters are used to provide these service and consumes enormous electricity. Datacenters contribute a large portion of the carbon footprint in the environment. Through virtual machine (VM) consolidation, datacenter energy consumption can be reduced via efficient resource management. VM selection policy is used to choose the VM that needs migration. In this research, we have proposed PbV mSp: A priority-based VM selection policy for VM consolidation. The PbV mSp is implemented in cloudsim and evaluated compared with well-known VM selection policies like gpa, gpammt, mimt, mums, and mxu. The results show that the proposed PbV mSp selection policy has outperformed the exisitng policies in terms of energy consumption and other metrics.

Internet-scale Computing Security - The analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.

Internet-scale Computing Security - The data of large-scale distributed demand-side iot devices are gradually migrated to the cloud. This cloud deployment mode makes it convenient for IoT devices to participate in the interaction between supply and demand, and at the same time exposes various vulnerabilities of IoT devices to the Internet, which can be easily accessed and manipulated by hackers to launch large-scale DDoS attacks. As an easy-to-understand supervised learning classification algorithm, KNN can obtain more accurate classification results without too many adjustment parameters, and has achieved many research achievements in the field of DDoS detection. However, in the face of high-dimensional data, this method has high operation cost, high cost and not practical. Aiming at this disadvantage, this chapter explores the potential of classical KNN algorithm in data storage structure, K-nearest neighbor search and hyperparameter optimization, and proposes an improved KNN algorithm for DDoS attack detection of demand-side IoT devices.

Internet-scale Computing Security - Using the methods of literature and interview, this paper analyzes the current situation of performance evaluation of volunteers in large-scale games based on mobile Internet, By analyzing the popularity of mobile Internet, the convenience of performance evaluation, the security and privacy of performance evaluation, this paper demonstrates the necessity of performance evaluation of volunteers in large-scale games based on mobile Internet, This paper puts forward the Countermeasures of performance evaluation of volunteers in large-scale games based on mobile Internet.

Internet-scale Computing Security - The scale of the intelligent networked vehicle market is expanding rapidly, and network security issues also follow. A Situational Awareness (SA) system can detect, identify, and respond to security risks from a global perspective. In view of the discrete and weak correlation characteristics of perceptual data, this paper uses the Fly Optimization Algorithm (FOA) based on dynamic adjustment of the optimization step size to improve the convergence speed, and optimizes the extraction model of security situation element of the Internet of Vehicles (IoV), based on Probabilistic Neural Network (PNN), to improve the accuracy of element extraction. Through the comparison of experimental algorithms, it is verified that the algorithm has fast convergence speed, high precision and good stability.

Internet-scale Computing Security - Cloud computing provides customers with enormous compute power and storage capacity, allowing them to deploy their computation and data-intensive applications without having to invest in infrastructure. Many firms use cloud computing as a means of relocating and maintaining resources outside of their enterprise, regardless of the cloud server s location. However, preserving the data in cloud leads to a number of issues related to data loss, accountability, security etc. Such fears become a great barrier to the adoption of the cloud services by users. Cloud computing offers a high scale storage facility for internet users with reference to the cost based on the usage of facilities provided. Privacy protection of a user s data is considered as a challenge as the internal operations offered by the service providers cannot be accessed by the users. Hence, it becomes necessary for monitoring the usage of the client s data in cloud. In this research, we suggest an effective cloud storage solution for accessing patient medical records across hospitals in different countries while maintaining data security and integrity. In the suggested system, multifactor authentication for user login to the cloud, homomorphic encryption for data storage with integrity verification, and integrity verification have all been implemented effectively. To illustrate the efficacy of the proposed strategy, an experimental investigation was conducted.

Internet-scale Computing Security - With billions of devices already connected to the network s edge, the Internet of Things (IoT) is shaping the future of pervasive computing. Nonetheless, IoT applications still cannot escape the need for the computing resources available at the fog layer. This becomes challenging since the fog nodes are not necessarily secure nor reliable, which widens even further the IoT threat surface. Moreover, the security risk appetite of heterogeneous IoT applications in different domains or deploy-ment contexts should not be assessed similarly. To respond to this challenge, this paper proposes a new approach to optimize the allocation of secure and reliable fog computing resources among IoT applications with varying security risk level. First, the security and reliability levels of fog nodes are quantitatively evaluated, and a security risk assessment methodology is defined for IoT services. Then, an online, incentive-compatible mechanism is designed to allocate secure fog resources to high-risk IoT offloading requests. Compared to the offline Vickrey auction, the proposed mechanism is computationally efficient and yields an acceptable approximation of the social welfare of IoT devices, allowing to attenuate security risk within the edge network.

Internet-scale Computing Security - The big data platform based on cloud computing realizes the storage, analysis and processing of massive data, and provides users with more efficient, accurate and intelligent Internet services. Combined with the characteristics of college teaching resource sharing platform based on cloud computing mode, the multi-faceted security defense strategy of the platform is studied from security management, security inspection and technical means. In the detection module, the optimization of the support vector machine is realized, the detection period is determined, the DDoS data traffic characteristics are extracted, and the source ID blacklist is established; the triggering of the defense mechanism in the defense module, the construction of the forwarder forwarding queue and the forwarder forwarding capability are realized. Reallocation.

With billions of devices already connected to the network's edge, the Internet of Things (IoT) is shaping the future of pervasive computing. Nonetheless, IoT applications still cannot escape the need for the computing resources available at the fog layer. This becomes challenging since the fog nodes are not necessarily secure nor reliable, which widens even further the IoT threat surface. Moreover, the security risk appetite of heterogeneous IoT applications in different domains or deploy-ment contexts should not be assessed similarly. To respond to this challenge, this paper proposes a new approach to optimize the allocation of secure and reliable fog computing resources among IoT applications with varying security risk level. First, the security and reliability levels of fog nodes are quantitatively evaluated, and a security risk assessment methodology is defined for IoT services. Then, an online, incentive-compatible mechanism is designed to allocate secure fog resources to high-risk IoT offloading requests. Compared to the offline Vickrey auction, the proposed mechanism is computationally efficient and yields an acceptable approximation of the social welfare of IoT devices, allowing to attenuate security risk within the edge network.

Cloud computing provides customers with enormous compute power and storage capacity, allowing them to deploy their computation and data-intensive applications without having to invest in infrastructure. Many firms use cloud computing as a means of relocating and maintaining resources outside of their enterprise, regardless of the cloud server's location. However, preserving the data in cloud leads to a number of issues related to data loss, accountability, security etc. Such fears become a great barrier to the adoption of the cloud services by users. Cloud computing offers a high scale storage facility for internet users with reference to the cost based on the usage of facilities provided. Privacy protection of a user's data is considered as a challenge as the internal operations offered by the service providers cannot be accessed by the users. Hence, it becomes necessary for monitoring the usage of the client's data in cloud. In this research, we suggest an effective cloud storage solution for accessing patient medical records across hospitals in different countries while maintaining data security and integrity. In the suggested system, multifactor authentication for user login to the cloud, homomorphic encryption for data storage with integrity verification, and integrity verification have all been implemented effectively. To illustrate the efficacy of the proposed strategy, an experimental investigation was conducted.

Web-based Application Programming Interfaces (APIs) are often described using SOAP, OpenAPI, and GraphQL specifications. These specifications provide a consistent way to define web services and enable automated fuzz testing. As such, many fuzzers take advantage of these specifications. However, in an enterprise setting, the tools are usually installed and scaled by individual teams, leading to duplication of efforts. There is a need for an enterprise-wide fuzz testing solution to provide shared, cost efficient, off-nominal testing at scale where fuzzers can be plugged-in as needed. Internet cloud-based fuzz testing-as-a-service solutions mitigate scalability concerns but are not always feasible as they require artifacts to be uploaded to external infrastructure. Typically, corporate policies prevent sharing artifacts with third parties due to cost, intellectual property, and security concerns. We utilize API specifications and combine them with cluster computing elasticity to build an automated, scalable framework that can fuzz multiple apps at once and retain the trust boundary of the enterprise.