Cloud Network Research Articles

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations between them. This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets. The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers. The proposed AKFA (Adaptive Kernel Firefly Algorithm) is for selecting features for CNM (Cloud Network Monitoring) operations. AKFA methodology is demonstrated using CNSD (Cloud Network Security Dataset) with satisfactory results in the performance metrics like precision, recall, F-measure and accuracy used.

A New IDS for Detecting DDoS Attacks in Wireless Networks using Spotted Hyena Optimization and Fuzzy Temporal CNN

<p>Cyber-attacks are rapidly increasing in the internet era due to the growth of information technology. The distributed denial of service (DDoS) attacks are increasing dramatically due to the distributed services in cloud networks. In this paper, a new Intrusion Detection System (IDS) is proposed to improve the performance of the networks by detecting DDoS attacks effectively in wireless networks. In this work, we propose a new feature selection method called Split Filter Feature Selection and Spotted Hyena Optimization Based Feature Optimization Method (SFSH-FOM) to select the most contributed features that are helpful for enhancing the classification accuracy. In this work, a new deep learning algorithm named Fuzzy Temporal Features incorporated Convolutional Neural Network (FT-CNN) is proposed for performing effective classification. Here, a new cross layer feature fusion technique is also proposed by using FT-CNN and LSTM for enhancing the performance. The experiments have been carried out to evaluate the proposed IDS using the standard datasets, namely the KDD’99 dataset, the NSL-KDD dataset, and the DDoS dataset by considering the evaluation metrics such as detection accuracy, recall, precision, and F1-score, and it has also been proved as better than other IDSs in terms of accuracy and false alarm rate.</p> <p> </p>

DCNNet: A Distributed Convolutional Neural Network for Remote Sensing Image Classification

With the development of information technology, multi-platform collaborative collection and processing of remote sensing images has become a significant trend. However, the existing models are challenging to achieve accurate and efficient image interpretation on remote sensing multi-platform systems. To solve this problem, we propose a novel distributed convolutional neural network (DCNNet) and demonstrate the superiority of our method in remote sensing image classification. Firstly, a progressive inference mechanism is introduced to support most images to be classified in advance with satisfactory accuracy, which minimises redundant cloud transmission and achieves higher inference acceleration. Meanwhile, a distributed self-distillation paradigm is designed to integrate and refine in-depth features, performing efficient knowledge transfer between terminals and the cloud network. Secondly, a multi-scale feature fusion (MSFF) module is presented to extract valid receptive fields and assign weights to crucial channel dimension features. Finally, a sampling augmentation (SA) attention is proposed to enhance the effective feature representation of RS images through a bottom-up and top-down feedforward structure. We conducted extensive experiments and visual analyses on three benchmark scene classification datasets and one fine-grained dataset. Compared with the existing methods, DCNNet consolidates several advantages in terms of accuracy, computation, transmission and processing efficiency into a single framework for multi-platform remote sensing image classification.

China Telecom’s Research and Applications of Business-Enterprise Full-Optical Networking

With the continuous development of optical fiber access and other network infrastructure, cloud conference, online education, online office, cloud VR and other innovative services requiring high bandwidth are widely developed, which puts forward higher requirements on network bandwidth, delay, jitter and stability. Upgrading the legacy optical fiber network, building a gigabit optical broadband network, and promoting the further extension of optical fiber network to the enterprises help to improve the operation efficiency of information infrastructure and solve the problem of network coverage. High-quality business and enterprise broadband services can be realized, which stimulate potential consumer demand, and lay a solid foundation for the sustainable development of internet services. The business-enterprise full-optical network technology and the applications are studied, the related industrial chain and standardization progress are introduced, and the application mode and development trend are analyzed in this paper.

Research on Four-Dimensional Innovative Intelligent Education Platform Based on Cloud Edge-End Architecture.

In order to solve the problem of backward talent training mode in agriculture-related colleges and universities, this paper proposed a scheme to build a smart teaching platform by using cloud architecture, combining virtualization and twinning technology. The intelligent teaching platform is developed using the 5G converged network architecture and cloud edge system architecture. The intelligent teaching platform has realized such teaching modes as real scene teaching, combination of virtual and real teaching, immersive teaching, multi-teacher collaborative teaching and live interactive teaching. The smart teaching platform has established a new model of digital education, with the functions of teaching, teaching research, teaching management and teaching evaluation, and provides smart teaching cloud services for teachers and students of agriculture-related colleges and universities as well as external tutors. The research of multi-dimensional evaluation system solves the precise management of teaching process. The teaching effect has been significantly improved, and the management cost has been reduced, which meets the goal of training new agricultural talents in agricultural and forestry colleges.

Optimized deep learning methodology for intruder behavior detection and classification in cloud

Apps, networks, frameworks, and services are all made possible by the cloud. In order to get the most out of the improved accessibility and computational capabilities, the Service Providers can offer an optimal use of current services. Application services have been revolutionised as their launch since they are useful and cost-effective for both suppliers and users. Increasingly, cyber defence is a vital research area in today’s environment, where networks are essential. The software and hardware of a network are constantly monitored by an intrusion detection framework (IDF), which is an essential part of any cyber defence plan. Many of the current IDSs are still struggling to improve detection performance, reduce false alarm rates and detect new threats. Based on parametric computation analysis, this study provides a deep learning approach to optimize cloud networks and to identify intruders. Results of suggested approach have been displayed from data compared to current methods in the conclusion section.

Can point cloud networks learn statistical shape models of anatomies?

Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying anatomical variations within populations of anatomies. However, traditional correspondence-based SSM generation methods have a prohibitive inference process and require complete geometric proxies (e.g., high-resolution binary volumes or surface meshes) as input shapes to construct the SSM. Unordered 3D point cloud representations of shapes are more easily acquired from various medical imaging practices (e.g., thresholded images and surface scanning). Point cloud deep networks have recently achieved remarkable success in learning permutation-invariant features for different point cloud tasks (e.g., completion, semantic segmentation, classification). However, their application to learning SSM from point clouds is to-date unexplored. In this work, we demonstrate that existing point cloud encoder-decoder-based completion networks can provide an untapped potential for SSM, capturing population-level statistical representations of shapes while reducing the inference burden and relaxing the input requirement. We discuss the limitations of these techniques to the SSM application and suggest future improvements. Our work paves the way for further exploration of point cloud deep learning for SSM, a promising avenue for advancing shape analysis literature and broadening SSM to diverse use cases.

Decentralized Control of Distributed Cloud Networks With Generalized Network Flows

Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly important role in the efficient delivery of real-time stream-processing applications (also referred to as augmented information services), such as industrial automation and metaverse experiences (e.g., extended reality, immersive gaming). While such applications require processed streams to be shared and simultaneously consumed by multiple users/devices, existing technologies lack efficient mechanisms to deal with their inherent multicast nature, leading to unnecessary traffic redundancy and network congestion. In this paper, we establish a unified framework for distributed cloud network control with generalized (mixed-cast) traffic flows that allows optimizing the distributed execution of the required packet processing, forwarding, and replication operations. We first characterize the enlarged multicast network stability region under the new control framework (with respect to its unicast counterpart). We then design a novel queuing system that allows scheduling data packets according to their current destination sets, and leverage Lyapunov drift-plus-penalty control theory to develop the first fully decentralized, throughput- and cost-optimal algorithm for multicast flow control. Numerical experiments validate analytical results and demonstrate the performance gain of the proposed design over existing network control policies.

PnP-3D: A Plug-and-Play for 3D Point Clouds.

With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis. However, there is great potential for development of these networks since the given information of point cloud data has not been fully exploited. To improve the effectiveness of existing networks in analyzing point cloud data, we propose a plug-and-play module, PnP-3D, aiming to refine the fundamental point cloud feature representations by involving more local context and global bilinear response from explicit 3D space and implicit feature space. To thoroughly evaluate our approach, we conduct experiments on three standard point cloud analysis tasks, including classification, semantic segmentation, and object detection, where we select three state-of-the-art networks from each task for evaluation. Serving as a plug-and-play module, PnP-3D can significantly boost the performances of established networks. In addition to achieving state-of-the-art results on four widely used point cloud benchmarks, we present comprehensive ablation studies and visualizations to demonstrate our approach's advantages. The code will be available at https://github.com/ShiQiu0419/pnp-3d.

Research on Secure Cloud Networking Plan Based on Industry-Specific Cloud Platform

Research on Secure Cloud Networking Plan Based on Industry-Specific Cloud Platform

Resource Management in UAV Enabled MEC Networks

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem. According to simulation results, both local and offloading schemes give optimal solution.

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection (EAGTODL-CTD) in IoT Cloud Networks. The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment. The proposed EAGTODL-CTD model mainly focuses on the conversion of input binary files to color images, where the malware can be detected using an image classification problem. The EAGTODL-CTD model pre-processes the input data to transform to a compatible format. For threat detection and classification, cascaded gated recurrent unit (CGRU) model is exploited to determine class labels. Finally, EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters, showing the novelty of our work. The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign. The experimental values reported the supremacy of the EAGTODL-CTD model with increased accuracy of 99.47%.

Framework of Geofence Service using Dummy Location Privacy Preservation in Vehicular Cloud Network

With the increasing prevalence of different mobile apps, many applications require users to enable the location service on their devices. For example, the geofence service can be defined as establishing virtual geographical boundaries. Enabling this service triggers entering and exiting the boundary area and notifies the users and trusted third parties. The foremost concern of using geofence is the privacy of location coordinates shared among different applications. In this paper, a framework called ‘TIET-GEO’ is proposed that allows users to define the geofence boundary; in addition, it monitors Global Positioning System (GPS) devices in real-time when they enter/exit a specific area. The proposed framework also proposes a dummy privacy preservation algorithm to generate K-dummy locations around the real trajectories when the user requests the Point Of Interest (POI) from the Location-Based Services (LBS). This article aims to enhance the location privacy preservation in geofence service, by generating a k-dummy location around the user location based on the radius size of the geofence area. The proposed framework uses token keys authentication to authorize the users in the Vehicular Cloud Network (VCN) service by generating secret token keys authentication between the client and services. The results obtained show the effectiveness of the proposed framework was on parameters like flexibility and reliability of responses from different sources, such as smart IoT devices and datasets.

An Efficient Technique to Prevent Data Misuse with Matrix Cipher Encryption Algorithms

Many symmetric and asymmetric encryption algorithms have been developed in cloud computing to transmit data in a secure form. Cloud cryptography is a data encryption mechanism that consists of different steps and prevents the attacker from misusing the data. This paper has developed an efficient algorithm to protect the data from invaders and secure the data from misuse. If this algorithm is applied to the cloud network, the attacker will not be able to access the data. To encrypt the data, the values of the bytes have been obtained by converting the plain text to ASCII. A key has been generated using the Non-Deterministic Bit Generator (NRBG) mechanism, and the key is XNORed with plain text bits, and then Bit toggling has been implemented. After that, an efficient matrix cipher encryption algorithm has been developed, and this algorithm has been applied to this text. The capability of this algorithm is that with its help, a key has been obtained from the plain text, and only by using this key can the data be decrypted in the first steps. A plain text key will never be used for another plain text. The data has been secured by implementing different mechanisms in both stages, and after that, a ciphertext has been obtained. At the end of the article, the latest technique will be compared with different techniques. There will be a discussion on how the present technique is better than all the other techniques; then, the conclusion will be drawn based on comparative analysis.

A Hybrid Gravitational Emulation Local Search‐Based Algorithm for Task Scheduling in Cloud Computing

The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.

Проблемы реализации облачных технологий в низкоорбитальных космических системах персональной спутниковой связи и передачи данных и телеуправление

Currently, a steady trend has been formed for targeted informatization of the intelligence community. This is explained by the fact that in the current conditions of the political, economic and technological environment, information technologies are considered as an innovative tool for increasing the capabilities and competitiveness of the state while saving money. Purpose of the study: At present, the creation of a glob- al low-orbit space information system, which is based on a multi-satellite orbital constellation of small-sized spacecraft based on the principle of cluster organization, is currently relevant. The essential difference between the global low-orbit space information systems a cloud network is that the existing cloud technologies are adapted for terrestrial non-mobile computers, among which a significant part plays the role of not computers, but simply data storage devices and "holders". Thus, the problem arises of ensuring the controllability of the GKNIS with limited computing resources for each of the individual spacecraft that make up the space system. It is unlikely that there will be an effective way to solve this problem by including separate "large" spacecraft in the space system, the mass and dimensions of which will allow installing a powerful onboard computer on board to allow such spacecraft to play the role of computing nodes within the orbital system. It is obvious that the economic viability of the GKNIS will primarily depend on the ability to use in its composition spacecraft of precisely small mass and dimensions, unified by the platform of service systems and produced in-line by a conveyor method. The development of such a technology could be a key moment in the creation of an economically viable global low-orbit space information system. Results: The paper deals with the problems of managing the management of a multifunctional satellite communication system in solving problems of information exchange using cloud technologies.

Towards An Optimal Latency-Energy Dynamic Offloading Scheme for Collaborative Cloud Networks

Towards An Optimal Latency-Energy Dynamic Offloading Scheme for Collaborative Cloud Networks

Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

Cloud computing is one of the most attractive and cost-saving models, which provides online services to end-users. Cloud computing allows the user to access data directly from any node. But nowadays, cloud security is one of the biggest issues that arise. Different types of malware are wreaking havoc on the clouds. Attacks on the cloud server are happening from both internal and external sides. This paper has developed a tool to prevent the cloud server from spamming attacks. When an attacker attempts to use different spamming techniques on a cloud server, the attacker will be intercepted through two effective techniques: Cloudflare and K-nearest neighbors (KNN) classification. Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks. However, the KNN classifiers will determine which area the spammer belongs to. At the end of the article, various prevention techniques for securing cloud servers will be discussed, a comparison will be made with different papers, a conclusion will be drawn based on different results.

Static Algorithm Allocation with Duplication in Robotic Network Cloud Systems

Robotic networks can be used to accomplish tasks that exceed the capacity of a single robot. In a robotic network, robots can work together to accomplish a common task. Cloud robotics allows robots to benefit from the massive storage and computing power of the cloud. Previous studies mainly focus on minimizing the cost of resource retrieval by robots by knowing the resource allocation in advance. Duplicating algorithms on multiple nodes can reduce the total time required to execute a task. We address the question of which algorithms should be duplicated and where the duplicates should be placed to improve overall performance. We have developed a procedure to answer wherein a robotic network cloud system should algorithms be executed and whether they should be duplicated to achieve optimal performance in terms of overall task execution time for all robots. Our proposed duplication procedure is optimal in the sense that the number of duplicated algorithms is minimal, while the result provides minimal overall completion time for all robots.

Retracted: Psychosocial Factors and Psychological Characteristics of Personality of Patients with Chronic Diseases Using Artificial Intelligence Data Mining Technology and Wireless Network Cloud Service Platform.

[This retracts the article DOI: 10.1155/2022/8418589.].