Many cloud services utilize an API gateway, which enables them to be offered to users through API platforms such as Platform as a Service (PaaS), Software as a service (SaaS), Infrastructure as a Service (IaaS) and cross-platforms APIs. APIs are designed for functionality and speed by developers who write a small portion of code, which has visibility and is secure. The code that is created from third-party software or libraries has no visibility, which makes it insecure. APIs are the most vulnerable points of attack, and many users are not aware of their insecurity. This paper reviews API security in cloud applications and discusses details of API vulnerabilities, existing security tools for API security to mitigate API attacks. The author’s study showed that most users are unaware of API insecurity, organizations lack resources and training to educate users about APIs, and organizations depend on the overall security of the network instead of the security of standalone APIs.
This paper describes the operation of a single Degree of Freedom (DOF) flexible robotic arm by the optimal controller. The derived model is based on Euler- Lagrange approach, while the first and second-order (super twisting) Sliding Mode Control (SMC) is proposed as a nonlinear control strategy. Exploration of a flexible robotic arm with using state-of-the-art controllers is essential for practical applications. This is even more refreshing when these arms have joints that work independently of each other to create a smooth connection between the joints, but it still ensures the natural properties like a real human arm. This system has many similarities with the flexible link system of the MIMO model in its operational state analysis. Control laws must be followed by logical rules in a coherent whole. The next step is to design a controller to fit the structure of the system. The author also compared the use of the above controller with a system without using any controller to determine the effectiveness of using the above controller.
INTRODUCTION: Traditional Cloud Systems are struggling to cope with the exponential growth of data in todays’ distributed application environment. The amount of data online has continuously increased since 2003. From an estimated 5 Exabyte in 2003 to 988 Exabyte in 2010. Presently it is estimated that 5 Exabyte of data are produced daily. To cope with such astronomical load of data, Distributed Storage Systems such as Amazon, Google and Microsoft Azure are becoming the de-facto method for the storage of data. Replication is the method used for providing redundancy. However Erasure Coding is a worthy alternative. OBJECTIVES: The purpose of this paper is to assess the most used distributed storage systems using different evaluation criteria and identifying how erasure code can be integrated into them. METHODS: This paper provides a survey of well-known Distributed Storage Systems by using the CAP (Consistency, Availability and Partition Tolerance) Theorem. We go by presenting the solution according to the objectives set and trade-off acknowledged by the designers. RESULTS: A comprehensive survey is presented using five evaluation criteria (design principle, data model, failure detection and recovery, consistency and security). Adoption of erasure code in Distributed Storage Systems is discussed and its advantages are deliberated. Several open challenges are also put forward. CONCLUSION: This paper provides researchers in the field with a comprehensive review of Distributed Storage Systems and how the adoption of erasure codes will enhance their capabilities.
This paper describes the investigation of a flexible robotic arm with Degree of Freedom (DOF) through Linear Quadratic Gaussian (LQG) regulator under the influence of noise signals with using diagrams below. The derived model is based on the Euler-Lagrange approach while LQG mode is proposed as a modern control method. A flexible robotic arm that is test in the modern regulator is essential for applications. This is even more interesting while these arms have joints that work independently of each other, but they still ensure a tight connection between the joints. Testing the stability characteristics of the system through this regulator helps readers determine the output signals of a system exactly. From here, the author came up with strategies the match the requirements. Although the initial stage of the operating cycle is not stable with the use of LQG regulator, most of later stages of this process of closed systems of robotic systems gradually become stable. In particular, adjusting LQG for flexible joint robot gives satisfactory simulation results. This promises a positive result of other joint robot. Neural networks applied in this paper have proven its effectiveness in information security issues. It is also very useful for tight control of possible situations such as a large scale attack on network systems, automated control systems. The main contributions of this paper have been clearly demonstrated through two strategies that need to be addressed: LQG, neural networks for the data of LQG regulator.The methods described above have not been previously published for this model. Therefore, this article was born to outline the above objectives and the plans have been satisfactorily implemented. These studies are important because they serve as a necessary database in the selection of control strategies according to the requirements. Simulation results are done by Matlab. The purpose of the study of this paper: the author has searched for a stability of the system through the above methods. The applied method is a thorough study of the structure of the system as well as the construction of algorithms to form the regulator. The result ia a reliable simulation of events after the system is controlled by these regulators.
Today, the classical control methods are still widely used because of their excellent performance in a working environment with conditions of geo-graphical distance. They are suitable for functions of the system: more flexible operating system, easy to perform, less unwanted ricks occur, the efficiency of controlling a system better. Besides the traditional control methods, the author has applied more modern and smarter algorithms such as artificial intelligence to control a system on the ground or a system moving in the air. In this paper, artificial neural network (ANN) is applied for a flight model to demonstrate its effectiveness in all cases. ANN in this article to show off its amazing application for flying devices. This is a useful method because it is highly secure. Simulation is done by Matlab.
Over the last decade, there has been an exponential growth in high-performance computing. Computing such as cloud and fog computing is gaining popularity, whereas, Cloud computing is a computing organization where a large number of systems are connected to the internet for application, data, and sto
With the development of innovative technology transformation like cloud and Internet of Things (IoT), more technology companies are pursuing research in employing such innovations. Smart homes and cities are just two examples of the many systems and technologies that the IoT can endorse. IoT - based smart objects communicate with other parts, such as proxies, portable devices, as well as data collectors. Although these components help to tackle a number of societal issues and offer users new, cutting-edge services, their confined processing power makes them susceptible to well-known privacy and security attacks. This in turn highlights the demand for a strong technical as well as legislative foundation and asserts the significance of validity and reliability in IoT. This paper provides an insight of the IoT, security, as well as privacy challenges, and also discusses the recommendations for IoT solutions. Further we also highlighting some unresolved problems that require further study.
Fake news has become one of the most serious issues in recent years, especially on social media. For example, during the covid-19 pandemic, a great deal of false information about the virus spread easily and quickly through the internet. In this area, researchers have given substantial answers to this problem utilizing various machine learning techniques. However, there are some gaps that need to be clarified. In the context of COVID-19 fake news detection, in this study, we present a comparison of four major machine learning algorithms: SVM, Nave Bayes, Logistic Regression, and Random Forest. We proposed four new machine learning models by combining these algorithms with two feature extraction techniques (TF-IDF and CountVectorizer). On three datasets, we tested the suggested models and analyzed their performance. According to the obtained results, we concluded that some properties of the used datasets can affect the obtained results. In addition, we find the best model overall.
Ports play a crucial role in the global economy and in facilitating international trade. However, a port exists within a complex ecosystem and there are challenges in managing operations here for any single objective, with primary goals including operational performance, cost, sustainability, safety, and even satisfaction. Each goal can be aligned with a stakeholder group, and all need to be managed in parallel for an overall effective, satisfying, and efficient port. However, opportunities in the chain of activities at ports which lead to these goals being achieved are not being protected or exploited, and until schemes are put in place to do so, a challenging working and living environment is allowed to persist in and around the port. This is having a damaging impact on both employee motivation and local resident satisfaction [1] [2]. Furthermore, due to the dependencies between each stakeholder, a negative experience for one can lead to a consequential negative reaction for others, and the effectiveness and efficiency of the entire ecosystem begins to decline. From a port management perspective, there is therefore a need to manage port activities without unnecessary delay due to the ripple effect and subsequent reactions on all stakeholders in the ecosystem. The aim of this article is to consider the complexity of the port environment from the perspective of stakeholders, with a view to recognising the ways that their needs can be targeted in parallel using cloud-driven Service Level Agreements (SLAs). Port Stakeholders
Cloud computing is one of the active research areas in High Performance Computing. It helps to share the resources globally in a distributed manner. In this paper, hybrid ACO with ANN is designed to ensure the best and the secured VM consolidation process. Initially, the objectives constraints for power consumption, resource deterioration and the SLA parameters are modelled for the Physical Machines and Virtual Machines. The selection of VMs is explored by the concept of Ant Colony Optimization that select the best VMs by the predefined SLA parameters. Also, the eavesdropping attack is also modulated in the cloud shared environment. The proposed hybrid ACO with ANN is implemented in CloudSim and it’s compared with the honeybee with GA and PSO with GA. The simulation results have proved the efficiency of the VMs consolidation process with the security constraints in terms of time-related on uptime and downtime of the servers.
In an effort to examine the spread of large-scale cyber attacks, researchers have created various taxonomies. These taxonomies are purposefully built to facilitate the understanding and the comparison of these attacks, and hence counter their spread. Yet, existing taxonomies focus mainly on the technical aspects of the attacks, with little or no information about how to defend against them. As such, the aim of this work is to extend existing taxonomies by incorporating new features pertaining the defense strategy, scale, and others. We will compare the proposed taxonomy with existing state of the art taxonomies. We also present the analysis of 174large cyber security attacks based on our taxonomy. Finally, we present a web tool that we developed to allow researchers to explore exiting data sets of attacks and contribute new ones. We are convinced that our work will allow researchers gain deeper insights into emerging attacks by facilitating their categorization, sharing and analysis, which results in boosting the defense efforts against cyber attack.
INTRODUCTION: Nowadays, the cloud computing paradigm encounters newer challenges in offering fault tolerance methods during service provisioning. The failures during service provisioning are unavoidable in the large-scale heterogeneous network. Therefore, the adoption of appropriate fault tolerance techniques can improve the provided service's efficiency and reliability. OBJECTIVES: Thus, fault tolerating metrics give better accuracy to enhance the QoS, where the three-tier fault-tolerance approach is proposed to resolve the various failures in service provisioning. METHODS: Initially, a Collector Model collects the request and ranks it based on the service to be provided. Secondly, the redundancy filter module is designed to filter out the request replication and avoid the unnecessary process to be carried out. Finally, the fairness resource allocation module is designed to perform the prominent request received from the users based on the available resources without service congestion. RESULTS: This three-tier model operates concurrently to handle the multiple requests from the users of various connected nodes. The experimental analysis demonstrates that the three-tier fault tolerance model can enhance the cloud reliability over the large-scale heterogeneous network by ensuring QoS. CONCLUSION: The well-realized fault tolerance approach can efficiently demonstrate the structural model and fault tolerance process over the computing environment, therefore enhancing the cloud extendibility. Moreover, the computing environment's failure is hugely complex, and failure has to be handled efficiently.
INTRODUCTION: The establishment of trusted cloud services pretends to provide high impactful service with better satisfaction to the web-users and cloud service providers. Moreover, various existing trust-aware algorithms use diverse QoS measurements and related attributes, leading to the complex selection of cloud services. OBJECTIVES: Thus, this research intends to propose a Trustful-Lightweight Cloud Service Provisioning algorithm (TL-CSP) using Service Optimizer (SO). METHODS: Initially, the QoS metrics are determined by evaluating attributes based on a ranking method based on the users' requests. It is performed with the computation of weighted coefficients of received requests from the users. The service optimization is performed using a global optimizer to assist the cloud users in selecting the service with better satisfaction. RESULTS: The proposed TL-CSP accuracy is validated and compared with the existing cloud service provisioning algorithm to measure the proposed model's efficiency. CONCLUSION: The simulation is carried out in a MATLAB environment. The proposed TL-CSP intends to shows a better trade-off in contrast to prevailing approaches.
The Internet of Things is a major development in information technology that increasingly dominates and reigns in the computer systems market. However, due to the threat of cyber attacks, the security of IoT is still one of the major issues holding back the evolution of this technology. For this, the authentication of objects is very important in IoT. In this paper, we propose a lightweight authentication protocol for the IoT Based Wireless Sensor Networks, called AuthenIoT. The objective is to provide mutual authentication services for connected objects. This protocol must take into account the constraints of the objects and the used communication technologies. To achieve such protocol, we opted for WSNs as an IoT use case. Furthermore, we demonstrate that the proposed scheme provides an efficient security for connected devices and that its computation and communication costs are suitable for extremely low-cost IoT devices.
The enterprise educational environment will involve big data technology, cloud computing, Internet of Things (IoT) and artificial intelligence (AI), which will characterize the current society extreme automation and enterprise productivity through reinventing the new world educational reform. This current paper captured the manageability issues in electronic learning environments and explored ways through which the managerial performances could be improved in the perspective of electronic learning investments through incorporation of context-awareness and self-reconfiguration adaptive systems. The aim is to allow the open source electronic learning system to provide educators, administrators, and learners with a single robust and integrated system for creating a personalized and autonomous learning accomplishments. The paper reviewed the contemporary development in electronic learning system in internet of things and cloud computing and established the prospect for the continued educational investment. A survey of four tertiary institution in the south eastern Nigeria provided a justification for the adoption of cloud computing technology as the best alternative approach for organizational data warehousing in the ongoing society automation. The result of the paper indicated that technology implementation in schools are fundamental to students’ academic accomplishment, which made it obviously imperative that teachers in the 21st century should adjust digitally and technologically and prepare students for the opportunities in the emerging digital new world. The paper concluded that the successful curriculum implementation in the twenty first century will require a blend of technology innovation and enterprise platform adaptability as potential leverages in achieving the global educational sustainability.
In recent years we have seen tremendous growth in two key technologies like Cloud and IoT h. Be that as it may, a few common points of interest getting from their integration have been recognized in the literature and are anticipated later on. From one perspective, IoT can profit by the boundless abilities and assets of the Cloud to repay its mechanical requirements (e.g., capacity, handling, vitality). In particular, the Cloud can offer a powerful answer for actualizing IoT service management and structure and additional applications that endeavor the things or the data created by them. Then again, the Cloud can profit by IoT by stretching out its extension to manage genuine things in a more disseminated and dynamic way, and for conveying new services in an expansive number of genuine situations. The integral attributes of Cloud and IoT emerging from the distinctive recommendations in literature and moving the Cloud IoT worldview are accounted for in this paper. The Cloud goes about as a middle layer between the things and the applications, where it conceals all the multifaceted nature and the functionalities important to actualize the last mentioned. This framework will affect future application advancement, where data social occasion, preparing, and transmission will create new difficulties to be tended to, likewise in a multi-cloud condition. In the accompanying, we condense the issues settled and the points of interest acquired while embracing the Cloud IoT worldview. Cutting edge technologies like Cloud & IoT has gained popularity in various businesses and there is great importance for organizations to understand the possible benefits which an organization can benefit by the adoption & implementation of new technologies like Cloud & IoT. This study focuses on a survey-based investigation of various businesses & IT leaders to identify the organizational performance post after the implementation of cloud & IoT. This study focuses on measuring key organizational performance metrics like profit, revenue, customer satisfaction, product delivery, product quality etc.
Cloud computing is a widely used technology today and thus, large number of applications are being stored in the cloud. To cater to the needs of the consumers an intermediate role known as cloud broker has been introduced. It helps to cut down overall expenses of the cloud users. The proposed system concentrates on configuration of the cloud broker to improve profit. This maximization scheme depends upon various factors like customer request, selling price of the resource, purchasing price of the resource, intensity of the request and so on. The system developed, aims at maximizing the profit of the cloud broker who services consumer requirements by providing cloud infrastructure at a lower cost from an infrastructure vendor. Availing pay-as-you-go schemes or on-demand payment has proven to be useful. The complex cloud landscape along with various billing schemes paves a way for a profit maximization model that incorporates temporal multiplexing by a middleman entity for a better economic upfront. The resource multiplexing is further enhanced by incorporating M/D/c queueing model and short-term and long-term renting schemes to optimize the resource allocation. The entire application has been implemented using Java especially the NetBeans IDE 8.2. Different algorithms were compared to further enhance the pricing scheme selection. Finally, Hill-Climbing algorithm is used to allocate the resources dynamically. The system further notifies the cloud broker on resource shortage through dynamic disk utilization graph. The proposed system considers quality of service and price of service as the determining factors in maximizing the net profit of the cloud broker.
Cloud computing emerges as a powerful platform to deliver IT services online. Due to the rapid development of cloud computing the user's dependence on the cloud has increased and hence user request per unit time is increases. Now scheduling and serving the user requests is a major challenge. Particle swarm optimization as a heuristic algorithm is the most suitable algorithm in such scenario to serve user requests for the most appropriate resources. Author written this research paper in continuation with previous research paper called Modified particle swarm optimization (MPSO) in which author controlled the inertia weight in PSO to find the best cost. This research paper investigates the effect of acceleration coefficient to achieve the best cost. The implementation results of PSO with different acceleration coefficient are produced and compared. Author has use MATLab to test the effect of acceleration coefficient on fitness value and also implemented in CloudSim simulator to test variation in execution time in various scenario. The purpose of author is also to test correctness of Reyes-Sierra and Coello [19] suggested acceleration coefficient.
Data storage in cloud is widely utilized by different commercial, educational, scientific, healthcare applications and much more. The major concern apart from data security is data integrity. Our work focuses on data integrity. In spite of the presence of Service Level Agreement (SLA) between the data owner and CSP, the data integrity may be affected and also todays’ cloud computing platform processes more of real-time data, which involves dynamic data operations. Most of the existing works intend to verify the cloud data integrity however the issues related to data replicas are not usually considered and most of the existing cloud techniques fail to focus on dynamic data operations. This article presents an outsourced auditing with data integrity verification scheme (OA-DIV) scheme that can handle multiple copies of cloud data and supports dynamic data operations such as data insertion, deletion and updation. The performance of the proposed work is tested with respect to communication cost and processing time, while comparing the results with the existing approaches.
Cloud computing delivers computing resources like software and hardware as a service to the users through a network. Due to the scale of the modern datacentres and their dynamic resources provisioning nature, we need efficient scheduling techniques to manage these resources. The main objective of scheduling is to assign tasks to adequate resources in order to achieve one or more optimization criteria. Scheduling is a challenging issue in the cloud environment, therefore many researchers have attempted to explore an optimal solution for task scheduling in the cloud environment. They have shown that traditional scheduling is not efficient in solving this problem and produce an optimal solution with polynomial time in the cloud environment. However, they introduced sub-optimal solutions within a short period of time. Meta-heuristic techniques have provided near-optimal or optimal solutions within an acceptable time for such problems. In this work, we have introduced the major concepts of resource scheduling and provided a comparative analysis of many task scheduling techniques based on different optimization criteria.
