Cloud Network Research Articles

Crypto Deep Ring Topology Firewall in Sensitive Data Transmission and Retrieval in Cloud

The data transmission and data retrieval process from the cloud is a critical issue because of cyber‐attacks. The data in the cloud is highly vulnerable and may fall prey to hackers. The hackers tend to attack the data in the public network, deteriorating the range of confidentiality and the authentication of the data. To prevent this attack on the cloud data, this manuscript proposes a crypto deep ring topology firewall to protect the cloud from data breaches. The data transmission has been performed using egress ring topology crypto encryption that solves the difficulty in isolating the traffic path between the edge and cloud network. Moreover, during the cloud data retrieval, the data interoperability issue arises due to the improper cloud service level agreement, which is solved using an application programing interface firewall fetch intrusion prevention system used in the secure transmission technique in which the data are entered into the transport and session layer of the firewall and then into the intrusion detection and prevention system thus sieving of data is carried out to solve the amenability violation of the cloud network and eliminate data interoperability issue. The proposed model was implemented in the Python platform and provided an enhanced level of encryption and decryption performance than the existing cloud retrieval model, producing high access speed to the cloud network with data security. The proposed work has proved to be highly robust against cyber attacks like man‐in‐the‐middle attacks and spoofing attacks.

Resilience and Recovery Mechanisms for Software-Defined Networking (SDN) and Cloud Networks

Resilience and Recovery Mechanisms for Software-Defined Networking (SDN) and Cloud Networks

Multi-cloud resource scheduling intelligent system with endogenous security

<abstract> <p>A secure and reliable intelligent multi-cloud resource scheduling system in cyberspace is especially important in some industry applications. However, this task has become exceedingly challenging due to the intricate nature of information, the variety of knowledge representations, the compatibility of diverse knowledge reasoning engines, and the numerous security threats found in cloud networks. In this paper, we applied the endogenous security theory to the multi-cloud resource scheduling intelligent system and presented a novel model of the system. The proposed model incorporates various knowledge representations and inference engines, resulting in a multi-cloud resource scheduling intelligent system that ensures endogenous security. In addition, we have devised a scheme for an intelligent system that schedules multi-cloud resources using dual-channels and has an endogenous security mechanism, which we have named Dynamic, Heterogeneous, and Redundant (DHR). Finally, we have used the multi-cloud resource scheduling intelligent run log database to carry out numerous experiments to validate the efficiency of the dual-channel redundant reasoning system with the endogenous security mechanism's DHR property. The results of the experiment demonstrated that the multi-cloud resource intelligent scheduling system model with an endogenous security mechanism was superior to the current single-channel inference system scheme in regards to security and reliability.</p> </abstract>

PMMNet: A Dual Branch Fusion Network of Point Cloud and Multi-View for Intracranial Aneurysm Classification and Segmentation.

Intracranial aneurysm (IA) is a vascular disease of the brain arteries caused by pathological vascular dilation, which can result in subarachnoid hemorrhage if ruptured. Automatically classification and segmentation of intracranial aneurysms are essential for their diagnosis and treatment. However, the majority of current research is focused on two-dimensional images, ignoring the 3D spatial information that is also critical. In this work, we propose a novel dual-branch fusion network called the Point Cloud and Multi-View Medical Neural Network (PMMNet) for IA classification and segmentation. Specifically, one branch based on 3D point clouds serves the purpose of extracting spatial features, whereas the other branch based on multi-view images acquires 2D pixel features. Ultimately, the two types of features are fused for IA classification and segmentation. To extract both local and global features from 3D point clouds, Multilayer Perceptron (MLP) and the attention mechanism are used in parallel. In addition, a SPSA module is proposed for multi-view image feature learning, which extracts more exquisite channel and spatial multi-scale features from 2D images. Experiments conducted on the IntrA dataset outperform other state-of-the-art methods, demonstrating that the proposed PMMNet exhibits strong superiority on the medical 3D dataset. We also obtain competitive results on public datasets, including ModelNet40, ModelNet10, and ShapeNetPart, which further validate the robustness and generality of the PMMNet.

CatBoost-based Intrusion Detection Method for the Physical Layer of Smart Agriculture

Agriculture holds a pivotal role in the progress of human society. The challenges stemming from a burgeoning population, land degradation, water scarcity, and urbanization have intensified the need for more efficient agricultural production. While smart farming brings significant benefits to farmers and agricultural output, it also introduces complex cybersecurity risks to agricultural production. The security of the physical layer in smart agriculture is intricately tied to crop growth and yield, with indirect implications for the security of the network and application layers. This paper introduces a novel intrusion detection scheme based on CatBoost for the physical layer and evaluates its effectiveness using the publicly available ToN_IOT dataset. In binary classification results, the scheme achieves a remarkable recognition accuracy of 99.94%, along with a precision and recall of 99.88%. In multi-classification results, the scheme outperforms other existing solutions across all metrics. The experimental findings clearly illustrate the exceptional recognition accuracy of this implemented method against physical layer attacks within the domain of smart agriculture. Furthermore, the system’s implementation ensures the security of input data for the smart agriculture network layer, cloud, and blockchain applications.

Mathematical Models for Predicting Network Traffic in Cloud Computing Environments

Effective network traffic prediction is crucial for optimizing resource allocation and ensuring efficient performance in cloud computing environments. In this research, mathematical models are developed to use historical data, factors, and other data to predict future network traffic patterns more effectively. In this context, we then compare an array of time series models such as ARIMA, LSTM, as well as the Prophet model so that we can determine the cloud environments most appropriate for each. These models include time and day of the week as well as the general activities of the users in the network in order to mimic the real flow of network traffics. The experimental results concern the efficiency of the proposed models as opposed to existing approaches and give a lot of information to network administrators and cloud service providers. The outcomes make a significant contribution as to the formulation of intelligent approaches in resource management and improve the dependability and performance of cloud computing environments.

Distribution-Aware Interactive Attention Network and Large-Scale Cloud Recognition Benchmark on FY-4A Satellite Image

Distribution-Aware Interactive Attention Network and Large-Scale Cloud Recognition Benchmark on FY-4A Satellite Image

SLA-Based Service Provisioning Optimization in Vehicular Cloud Networks Using Fuzzy Logic

SLA-Based Service Provisioning Optimization in Vehicular Cloud Networks Using Fuzzy Logic

Corrections to “Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques—Recent Research Advancements”

Corrections to “Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques—Recent Research Advancements”

Balancing the Load and Scheduling the Tasks using Zebra Optimizer in IoT based Cloud Computing for Big-data Applications.

Task scheduling is one of the major problems with Internet of Things (IoT) cloud computing. The need for cloud storage has skyrocketed due to recent advancements in IoT-based technology. Sophisticated planning approaches are needed to load the IoT services onto cloud resources professionally while meeting application necessities. This is significant because, in order to optimise resource utilisation and reduce waiting times, several procedures must be properly configured on various virtual machines. Because of the diverse nature of IoT, scheduling various IoT application activities in a cloud-based computing architecture can be challenging. Fog cloud computing is projected for the integration of fog besides cloud networks to address these expectations, given the proliferation of IoT sensors and the requirement for fast and dependable information access. Given the complexity of job scheduling, it can be difficult to determine the best course of action, particularly for big data systems. The behaviour of zebras in the wild serves as the primary basis of stimulus for the development of the Zebra Optimisation Algorithm (ZOA), a novel bio-inspired metaheuristic procedure presented in this study. ZOA mimics zebras' feeding habits and their defence mechanisms against predators. Various activities are analysed and processed using an optimised scheduling model based on ZOA to minimise energy expenditures and end-to-end delay. To reduce makespan and increase resource consumption, the technique uses a multi-objective strategy. By using a regional exploratory search strategy, the optimisation algorithm may better utilise data and stays out of local optimisation ruts. The analysis revealed that the suggested ZOA outperformed other well-known algorithms. It was advantageous for big data task scheduling scenarios since it converged more quickly than other techniques. It also produced improvements of 18.43% in several outcomes, including resource utilisation, energy consumption, and make span.

AASeg: Artery-aware Global-to-Local Framework for Aneurysm Segmentation in Head and Neck CTA Images.

Aneurysm segmentation in computed tomography angiography (CTA) images is essential for medical intervention aimed at preventing subarachnoid hemorrhages. However, most existing studies tend to overlook the topological characteristics of arteries related to aneurysms, often resulting in suboptimal performance in aneurysm segmentation. To address this challenge, we propose an arteryaware global-to-local framework for aneurysm segmentation (AASeg) using CTA images of head and neck. This framework consists of two key components: 1) a centerline graph network (CG-Net) for aneurysm global localization, and 2) a point cloud network (PC-Net) for local aneurysm segmentation. The centerline graph is generated by extracting artery centerline structures from vessel masks obtained through a pre-trained model for head and neck vessel segmentation. This representation serves as a high-level representation of the artery structure, allowing for analysis of aneurysms along the entire arteries. It facilitates aneurysm localization via aneurysm-segment graph classification along the arteries. Then, local region of aneurysm segment can be sampled from the vessel mask according to the aneurysm-segment graph. Subsequently, aneurysm segmentation is performed on the point cloud constructed from the aneurysm segment through the PC-Net. Extensive experiments show that the proposed framework achieves state-of-the-art performance in aneurysm localization on a main dataset and an external testing dataset, with Recall of 84.1% and 80.7%, false positives per case of 1.72 and 1.69, and segmentation DSC of 66.1% and 60.2%, respectively.

Організаційно-педагогічні умови оптимізації використання дистанційних технологій у процесі самоосвітньої діяльності студентів

The article deals with the necessity to optimize self-education activity of pedagogical university students’ in the context of innovative technologies introduction, time deficit, and the need for continuous professional development of the specialists. It presents the approaches of major Ukrainian researchers to the definitions and features of higher education students’ independent work. The attention is drawn to the project technology, in particular to the web-quest, which is considered to be an effective method of organizing students’ independent work at a pedagogical university. The organizational and pedagogical prerequisites for optimizing the effectiveness of pedagogical university students’ independent work are defined as follows: involvement of students in project activities based on an interdisciplinary approach; application of cloud network technologies in the organization of project activities of future teachers based on an interdisciplinary approach; the use of universal cognitive and metacognitive strategies of self-education in the process of project activities organized by means of cloud technologies; combination of both collective and individual forms of cognition in the project activities, which provides opportunities for self-actualization of the future teacher’s personality. The article summarizes the features that prove the feasibility of using web-quests in the independent study activity of prospective teachers: problem-based tasks; the role guidance; combination of individual and group forms of independent work; accumulation of different types of activity (cognitive, data search, analytical and exploratory). The study discusses a modern effective form of organizing students' independent work – an interdisciplinary web-quest, aimed at finding solutions to specific problems, the scope of which goes beyond the limits of a particular field of knowledge. The peculiar features of students’ and teachers’ activities are analyzed in accordance with the structure of the interdisciplinary web-quest (objectives, content, information, presentation components). Keywords: organizational and pedagogical prerequisites, independent work, students, optimization, project technology, web-quest, interdisciplinarity, cloud technologies.

Intrusion Optimal Path Attack detection using ACO for Cloud Computing

As the cloud infrastructure is simultaneously shared by millions of consumers, heinous use of cloud resources are also increasing. It makes ways to attackers to set up attacks by exploiting the vulnerabilities. And obviously, these attacks are leading to severe disasters as innocent consumers are unknowingly sharing cloud resources with harmful attackers. To prevent the occurrence of cloud attacks, attack graph based framework is proposed in this paper. Here, an attack path sketches an attack scenario by a streak of threats ranging in severity rating that shows how popular a particular cloud network service is in comparison. In a dynamic cloud environment, the proposed framework can disclose an optimal attack path thereby preventing cloud attacks. In cloud system the infrastructure is shared by potentially millions of users, which benefits the attackers to exploit vulnerabilities of the cloud. An instrument for analyzing multi-stage, multi-host assault scenarios in networks is the attack graph. It might not be possible for the administrator to patch every vulnerability n a large number of assault paths in an attack graph. The administrator might not be able to fix every vulnerability. To identify the most preferred or ideal assault path from a particular attack graph in a setting Ant Colony Optimization (ACO) algorithm is used.

Energy Efficient Task Scheduling Strategy using Modified Coot Optimization Algorithm for Cloud Computing

Cloud computing (CC) refers to a current computing method that provides the virtualization of computing services as a utility to Cloud service users. Problems based on ineffective task mapping to cloud resource frequently happen in a cloud atmosphere. Task scheduling (TS), thus, means effective scheduling of rational allocation and computational actions of computing resource in certain limitations in the IaaS cloud network. Job scheduling was to allocate tasks to the most appropriate sources to reach more than one goal. Thus, choosing a suitable work scheduling technique for rising CC resource efficiency, whereas maintaining high quality of service (QoS) assurances, becomes a significant problem that remains to attract interest of researchers. Metaheuristic techniques shown remarkable efficacy in supplying near-optimal scheduling solutions for a complicated large-sized issues. Recently, a rising number of independent scholar has examined the QoS rendered by TS approaches. Therefore, this study develops an Energy Efficient Task Scheduling Strategy using Modified Coot Optimization Algorithm (EETSS-MCOA) for CC environment. The EETSS-MCOA method carries out the derivation of features and MCOA is applied to schedule tasks. In addition, the MCOA algorithm is derived by the combination of adaptive β hill climbing concept with the COA for enhanced task scheduling. The conventional COA is stimulated by the swarming characteristics of birds known as coots. The COA followed two distinct stages of bird movements on water surface. The experimental results of the EETSS-MCOA model are validated on CloudSim tool. The solutions attained by the EETSS-MCOA model are found to be better than the existing algorithms.

Cloud-based mathematical models for self-organizing swarms of UAVs: design and analysis

Unmanned aerial vehicle (UAV) swarms have gained significant attention for their potential applications in various fields. The effective coordination and control of UAV swarms require the development of robust mathematical models that can capture their complex dynamics. The paper introduces mathematical models and relevant paradigms based on the design and analysis of self-organizing swarms of UAVs. The logical and technological construction of the model relies on the theorems developed by authors for obtaining full information exchange during the swarm quasi-random walk. The suggested rotor-router model interprets the discrete-time walk accompanied by the deterministic evolution of configurations of rotors randomly placed on the vertices of the swarm graph. The recommended optimal and fault-tolerant gossip/broadcast schemes support the resilience of swarm to internal failures and external attacks, and cryptographic protocols approve the security. The proposed cloud network topology serves as the implementation framework for the model, encompassing various connectivity options to ensure the expected behavior of the UAV swarms.

An Efficient Intrusion Detection System in Cloud Network Based on Deep Learning and Improved Marine Predators-Particle Swarm Optimization

An Efficient Intrusion Detection System in Cloud Network Based on Deep Learning and Improved Marine Predators-Particle Swarm Optimization

A Machine Learning based CIDS Model for Intrusion Detection to Ensure Security within Cloud Network

Cloud computing has evolved rapidly in recent years, and security concerns have gained prominence. Cloud computing offers the incredible capacity to provide inexpensive, easy-to-manage, flexible, adaptive, and powerful assets on the move via the Internet. Through effective and shared utilization, cloud computing maximizes the potential of hardware resources. Many issues in cloud computing raise are concerned regarding data and service availability. Numerous security services are needed to enhance cloud security for both users and service providers. One of the most essential research to establish security within cloud to avoid load imbalance situation through robust Cloud Intrusion Detection Systems (CIDS). Denial of service, scanning, malware code injection, virus, worm, and password cracking is prevalent cloud security issues. These attacks might jeopardize the company's reputation and inflict financial damage if not identified in time. Protecting the cloud from these kinds of threats, early detection and true prediction of such threats is the key goal of our proposal through this paper. It has been observed from earlier research proposals that dimensionality reduction is applied in conjunction with Data Mining (DM) and ML approaches found more perfomant in order to create such a robust model to ensure cloud network, authors suggested an CIDS by selecting appropriate features utilizing pertinent feature reduction approaches, then feeding this subset of data through the ML tool. 
 The simulation of the suggested model through ‘Python’ using ‘Scikit-Learn’ tool. Outcome of simulation experimental has been measured using various performance evaluation metric such as Precision, Recall, F-Score, Detection Ratio, RoC-curve etc. using KDDcup99 dataset as a benchmark. Simulation results from our suggested methodology were found more effective and comparable to several other existing methodologies.
 It has observed that ML based proposed model found capable enough to protect the cloud-based information by uncovering suspicious user behaviour interns as an outcome, secure cloud network against the threat and also found more performant in true prediction and early intrusion detection resultant reduction of computational cost.

Development of a Low-Cost, Open-Source LoRA-based SCADA System for Remote Monitoring of a Hybrid Power System for an Offshore Aquaculture Site in Newfoundland

In this article a low-cost and open-source Internet of Things (IoT) based Supervisory, Control and Data Acquisition (SCADA) system for remote monitoring of the hybrid power system for an offshore aquaculture site is presented. The selected site is situated 2 km away from the coastline where there is no electrical utility infrastructure and limited communication options are available. The hardware of the designed system primarily consists of six field sensors, Arduino Leonardo as Remote Terminal Unit (RTU), LoRA (Long Range) gateway, cables, AC/DC current and voltage supplies. Arduino IDE, AWS, Influx DB, and Grafana provide the software support. The field sensors are responsible for measuring the solar, battery, inverter & generator currents, along with battery voltage and temperature. All of the field sensors except the temperature sensor send the data to RTU which further delivers it to The Things Network (TTN) cloud. With the help of influx DB, AWS cloud computing services, and Grafana, the data can be stored and visualized through interactive yet informative graphs. The graphs display the historical and live data of each sensor. Further, it also gives the option to set alarms and alerts on user-defined conditions to improve control over the hybrid power system. The complete hardware is assembled and tested in Memorial University’s Power lab. The developed system was supplied with variable current/voltage supplies and the data was logged for three continuous hours. However, the data can be stored for a much longer duration as per user’s requirement. The hardware and the results presented here are a testament that the proposed design system is capable of providing a remote monitoring solution for the offshore aquaculture site.

Research on the Construction of University Cultural Brand IP Driven by Network Cloud Platform Technology

Abstract Campus culture brand IP, as the soft power in the construction of university brand culture, has become an important material carrier for the inheritance and dissemination of university brand culture. In the cloud platform technology, we are using an information diffusion model to explore the diffusion degree and influence of college culture brand IP information on the network platform. The dissemination process of cultural brand IP information is examined from the point of view of sharing information resources in colleges and universities. Construct a competitive information dissemination system based on the user nodes of online social networks and the interrelationship between users. Promote the dissemination and diffusion of university cultural brand IP information on online social networks using a data-driven approach. The results show that: k value is 0.12, the promotion effect of college culture brand IP is 2.59621e+04, and the relative risk index is 0.354264. Within 0-6 hours, the maximum number of IP views reaches 34,000 times, which indicates that the authority of the college platform and the information diffusion model can improve the diffusion of college culture brand IP.

Performance of the PERSIANN Family of Products over the Mekong River Basin and Their Application for the Analysis of Trends in Extreme Precipitation Indices

Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System) and PDIR-Now (PERSIANN-Dynamic Infrared Rain Rate near-real-time)—were compared to each other and evaluated against IMERG Final (Integrated Multi-satellite Retrievals for Global Precipitation Measurement—Final Run) from 2015 to 2020 over the Mekong River Basin and Delta (MRB) using a spatial resolution of 0.1∘ by 0.1∘ and at a daily scale. PERSIANN-CDR (PERSIANN-Climate Data Record) was also included in the evaluation but was not compared against the real-time products. In this evaluation, PDIR-Now exhibited a superior performance to that of PERSIANN-CCS, and the performance of PERSIANN-CDR was deemed satisfactory. The second part of the study entailed performing a Mann–Kendall trend test of extreme precipitation indices using 38 years of PERSIANN-CDR data over the MRB. This annual trend analysis showed that extreme precipitation over the 95th and 99th percentiles has decreased over the Upper Mekong River Basin, and the consecutive number of wet days has increased over the Lower Mekong River Basin.