Cloud Network Research Articles

Timely Detection of DDoS Attacks with Dimenstionality Reduction

Due to the interconnectedness and exponential proliferation of IoT devices, the technology is more susceptible to network attacks like Distributed Denial of Service (DDoS), which disrupt network resources. A growing threat to cloud computing systems is the Distributed Denial of Service (DDoS) attack, in which the attacker starts the attack by taking advantage of computers both inside and outside the cloud system. Real-time analysis of cloud network data is essential for preventing DDoS attacks. DDoS attacks interfere with the operation of Io- connected apps and services by taking advantage of the constrained resources on IoT devices. The impacts of DDoS attacks, which seriously damage current systems, are thoroughly examined in this article in the context of the Internet of Things. One of the most common network attacks is the distributed denial-of-service attack (DDoS). DDoS assaults intensified due to the quick development of computer and communication technologies. Therefore, investigating the detection of a DDoS attack is crucial. A single technique cannot offer adequate security due to the variety of DDoS attack techniques.

Cloud IaaS Optimization Using Machine Vision at the IoT Edge and the Grid Sensing Algorithm.

Security grids consisting of High-Definition (HD) Internet of Things (IoT) cameras are gaining popularity for organizational perimeter surveillance and security monitoring. Transmitting HD video data to cloud infrastructure requires high bandwidth and more storage space than text, audio, and image data. It becomes more challenging for large-scale organizations with massive security grids to minimize cloud network bandwidth and storage costs. This paper presents an application of Machine Vision at the IoT Edge (Mez) technology in association with a novel Grid Sensing (GRS) algorithm to optimize cloud Infrastructure as a Service (IaaS) resource allocation, leading to cost minimization. Experimental results demonstrated a 31.29% reduction in bandwidth and a 22.43% reduction in storage requirements. The Mez technology offers a network latency feedback module with knobs for transforming video frames to adjust to the latency sensitivity. The association of the GRS algorithm introduces its compatibility in the IoT camera-driven security grid by automatically ranking the existing bandwidth requirements by different IoT nodes. As a result, the proposed system minimizes the entire grid's throughput, contributing to significant cloud resource optimization.

Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud.

Enhancing Machine Learning-Based Autoscaling for Cloud Resource Orchestration

Performance and cost-effectiveness are sustained by efficient management of resources in cloud computing. Current autoscaling approaches, when trying to balance between the consumption of resources and QoS requirements, usually fall short and end up being inefficient and leading to service disruptions. The existing literature has primarily focuses on static metrics and/or proactive scaling approaches which do not align with dynamically changing tasks, jobs or service calls. The key concept of our approach is the use of statistical analysis to select the most relevant metrics for the specific application being scaled. We demonstrated that different applications require different metrics to accurately estimate the necessary resources, highlighting that what is critical for an application may not be for the other. The proper metrics selection for control mechanism which regulates the requried recources of application are described in this study. Introduced selection mechanism enables us to improve previously designed autoscaler by allowing them to react more quickly to sudden load changes, use fewer resources, and maintain more stable service QoS due to the more accurate machine learning models. We compared our method with previous approaches through a carefully designed series of experiments, and the results showed that this approach brings significant improvements, such as reducing QoS violations by up to 80% and reducing VM usage by 3% to 50%. Testing and measurements were conducted on the Hungarian Research Network (HUN-REN) Cloud, which supports the operation of over 300 scientific projects.

A COMPREHENSIVE REVIEW OF MACHINE LEARNING AND DEEP LEARNING APPLICATIONS IN CYBERSECURITY: AN INTERDISCIPLINARY APPROACH

Cybersecurity is increasingly becoming a critical concern as the complexity and frequency of cyber-attacks continue to rise. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to enhance cybersecurity systems, offering dynamic capabilities in real-time threat detection, anomaly detection, and intrusion prevention. This article (45) presents a systematic review of the applications of ML and DL in cybersecurity, adhering to the PRISMA guidelines. The review covers several key domains, including network security, cloud security, and Internet of Things (IoT) security, highlighting how ML/DL models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) outperform traditional rule-based systems. It also addresses challenges such as adversarial attacks, data privacy concerns, and the computational resource demands of DL models. Current solutions like adversarial training, federated learning, and model optimization techniques are examined for their potential to mitigate these issues. The findings suggest that while ML/DL technologies hold great promise, further research and innovation are necessary to overcome the inherent challenges, ensuring that these systems can be deployed effectively and securely in real-world environments.

SITF: A Self-Supervised Iterative Training Framework for Point Cloud Denoising

Despite existing supervised point cloud denoising methods having made great progress, they require paired ideal noisy-clean datasets for training which is expensive and impractical in real-world applications. Moreover, they may perform the denoising process multiple times with fixed network parameters for better denoising results at test time. To address above issues, this paper proposes a self-supervised iterative training framework (SITF) for point cloud denoising, which only requires single noisy point clouds and a noise model. Given an off-the-shelf denoising network and original noisy point clouds, firstly, an intermediate noisier-noisy dataset is created by adding additional noises from the known noise model to noisy point clouds (i.e. learning targets). Secondly, after training on the noisier-noisy dataset, the denoising network is employed to denoise the original noisy point clouds to obtain the learning targets for the next iteration. The above two steps are iteratively and alternatively performed to get a better and better trained denoising network. Furthermore, to get better learning targets for the next round, this paper also proposes a novel iterative denoising network (IDN) architecture of stacked source attention denoising modules. The IDN explicitly models the iterative denoising process internally within a single network via reforming the given denoising network. Experimental results show that existing supervised networks trained through the SITF can achieve competitive denoising results and even outperform supervised networks under high noise conditions. The source code can be found at: https://github.com/VCG-NJUST/SITF.

Research on Collaborative Defense Method of Hospital Network Cloud based on End-to-end Edge Computing

This research introduces a groundbreaking collaborative defense mechanism that utilizes end-to-end edge computing to bolster the security of decentralized hospital cloud systems. By integrating intrusion detection systems, firewalls, anomaly detection, and threat intelligence in a unified manner through the efficiency of edge computing, this approach marks a significant advancement in healthcare cybersecurity. Through rigorous testing with a substantial dataset, the system demonstrated exceptional performance metrics, including a remarkable 95% accuracy in threat detection, a low false positive rate, and a swift response time of merely 0.25 seconds. Notably, the system effectively mitigates computational overhead, thereby optimizing resource utilization. Comparative analysis with existing methodologies underscores the superiority of this novel framework, particularly in terms of geolocation accuracy, the minimization of false positives, and expedited reaction capabilities. This study’s collaborative defense strategy, underpinned by end-to-end edge computing, presents a holistic and innovative solution to the escalating cyber threats facing healthcare infrastructures. By redefining the parameters of security in medical settings, it paves the way for a safer and more resilient healthcare information technology ecosystem.

Minimizing Costs in Fog Nodes Through Effective Resource and Task Scheduling

Background: This system comprises the fog control node, access node, and Internet of Things (IoT) application. This study aims to reduce scheduling-related cloud costs. A user scheduling method and an economical task are carried out for this reason. The algorithm for job scheduling is constructed to determine the best strategy to handle several jobs in a fog access node. Reallocation technology ultimately reduces delays in both time and service. For analytical objectives, extensive simulations were run and performance metrics were compared with those of other currently in use methods. It was discovered that the suggested technique lowers task latency and offers extreme cost-effectiveness by improving the concurrent capability in the fog node, users, and task scheduling with improved performance statistics that made it possible. Materials and Methods: The research paper explores fog computing, a three-layered architecture consisting of IoT devices, fog computing, and cloud computing. The fog computing layer sits between cloud networks and Internet of Things (IoT) devices, providing fast access to these devices and handling computation locally at the network edge. Results: The research paper focuses on reducing cloud costs in scheduling. Although the proposed system showed many similarities in execution times compared to the diverse early completion-time method, it consistently lags behind the heterogeneous method by 5 to 10%. Conclusion: This research aims to reduce the cloud scheduling costs for Internet of Things (IoT) devices. A user-based scheduling algorithm and economical task allocation are implemented. A constraint-based and user-defined strategy is recommended.

RANCANG BANGUN APLIKASI MOBILE PEMBELAJARAN HANACARAKA BALI MENGGUNAKAN METODE CNN BERBASIS CLOUD COMPUTING

This research aims to develop a mobile application for learning Balinese Hanacaraka, designed to be engaging for students and to address the shortcomings of conventional learning methods, which are often less appealing. The application leverages Convolutional Neural Network (CNN) technology and cloud computing. TensorFlow is used for developing the CNN model, Android Studio for application development, and Google Cloud Platform (GCP) and Firebase for cloud services. The application offers four main features: writing practice, quizzes, statistics, and a dictionary. The CNN is divided into six models for Hanacaraka speech recognition, achieving an accuracy rate of 97-100% and an F1 score between 0.94-1. Testing results indicate that the application functions as expected, with a System Usability Scale (SUS) score of 70.57, suggesting user acceptance. API endpoints were tested using Postman and found to be reliable.

Role of Artificial Intelligence in the Computer Network: Review

A combination Artificial Intelligence (AI) with networks systems increases from efficiency and performance of the networks. Today routine life needs huge of data and applications shared between people that lead to a traffic load on networks, therefore intelligence networks produces are important abilities of AI in controlling and avoiding troubleshooting. Also AI improves from security and management of databases .Effective high-intelligent networks operate networks in automation systems .It is one of the most hopeful computer networks future. The development of artificial intelligence in the computer network relies on the importance of applications security and cloud computing. In recent years, a big development has occurred in Artificial Intelligence and majority of computer networks that leads to the of AI in the applications of network techniques. This paper reviews a role of combination of Artificial Intelligence with Computer Networks. Discussed two topics combination of AI in the computer network (Cloud Computing and Security). AI in cloud computing increases efficiency and significant networking. In addition, reduced the cost and minimized base of clouding. Artificial Intelligence controls huge amounts of databases in the cloud avoiding problems in heavy traffic networks. AI is a good solution in cyber-attacks, also solving most problems of security. Today more intelligent interaction with computer networks. Automate solutions in more application applying successful training and testing data sets. Artificial intelligence contributes to all fields of life and encourages the development of new technologies and uses. More robots, data automation, and many applications in the Internet of Things are expected.

Enhancing cloud network security with a trust-based service mechanism using k-anonymity and statistical machine learning approach

Enhancing cloud network security with a trust-based service mechanism using k-anonymity and statistical machine learning approach

Intelligent Forest Hospital as a New Management System for Hospital-Acquired Infection Control.

Hospital-acquired infection (HAI) is a significant global health concern, elevating the risks of morbidity and imposing a substantial socioeconomic burden. To enhance the management of HAI, particularly in the aftermath of the coronavirus disease 2019 (COVID-19) pandemic, the Guangdong Second Provincial General Hospital (GD2H) has launched a new system called Intelligent Forest Hospital (IFH). Leveraging advancements in artificial intelligence, 5G technology, and cloud networking, the IFH implements customized indoor air quality (IAQ) control strategies tailored to different medical settings. It utilizes various intelligent disinfection devices and air purification systems. The IFH features a dynamic 3D hospital model with real-time monitoring of crucial IAQ parameters and a risk assessment ranking for clinical departments, providing timely risk alerts, communication prompts, and automatic disinfection processes. The IFH aims to effectively mitigate HAI post-COVID-19 and other future pandemics, ensuring a safe and pleasant environment for patients, hospital staff, and visitors.

Artificial Intelligence Based Multi-Layer Approach for Finding Unknown Attacks in Cloud Network by Using Hybrid Intrusion Detection

Objectives : The objective of this study is to explore Intrusion Detection Systems and their various types by gathering research from previously published articles in refereed journals. The focus is on developing a proposed model capable of identifying unknown attacks in cloud networks using Signature and Anomaly-based Intrusion Detection Systems. Subsequently, the efficiency of the proposed model will be assessed, and a comparison with existing models will be conducted. The paper's main objective is to identify unknown attacks in a cloud network using a combination of signature and anomaly-based intrusion detection systems in an artificial intelligence-based multi-layered approach. Methods: Leveraging insights from existing literature, the proposed model combines signature-based IDS for known threat detection and anomaly-based IDS for detecting unusual behavioral patterns indicative of new or unseen attacks. Experimental evaluations using NSL-KDD and ADFA datasets demonstrate competitive accuracy and detection rates, with the proposed artificial intelligence-based Hybrid IDS achieving high performance in detecting both normal and malicious activities. Findings: This model produces above 90%, 96%, and 98% efficiency in the wired, Wireless, and Cloud networks respectively, and this model finds known attacks effectively while using parameters like event logs, file transferring time, TCP and UDP addresses, CPU Usage, Weak and synthetic data, IP and MAC address. Existing literature said that the existing model using the Hybrid Intrusion detection model can identify unknown attacks with a maximum of 80%, 92%, and 96% accuracy respectively. The findings suggest that the artificial intelligence-based multi-layered approach offers a promising solution for enhancing cloud network security, with the potential for further optimization and integration of advanced technologies in future research endeavors. Novelty: This study presents an artificial intelligence-based multi-layered approach for detecting unknown attacks in cloud networks by integrating signature-based and anomaly-based intrusion detection systems (IDS). The authors developed the model to detect the intrusion by using the Behaviour Profiling algorithm and dynamically prevent the data from intrusion by using the Statistical approach model. The authors trying to find unknown attacks, therefore the authors defined the objective of this paper as to find the unknown attacks in cloud networks by using the combination of signature and anomaly-based intrusion detection systems. The objective is to develop a model capable of effectively identifying cyber threats in cloud environments. The existing models do not concentrate on identifying unknown attacks by using Signature-based Intrusion Detection. Very few of the literature said that known attacks can be identified easily by using Signature-based Intrusion Detection but the unknown attacks identifying process is hard. Some of the Literature said that using the Hybrid Intrusion detection model can identify unknown attacks with a maximum of 80%, 92%, and 96% accuracy respectively. The current paper identifies unknown attacks in the cloud network using a combination of signature and anomaly-based intrusion detection systems in an artificial intelligence-based multi-layered approach and it produced above 97% accuracy. The unique feature of the model is artificial intelligence-based multi-layered approach and dynamic key have been utilized to avoid malicious activities in the network. Keywords: Anomaly based IDS, Cloud Network Security, Hybrid IDS, Multi-Layer Approach, Signature based IDS

Efficient microservices offloading for cost optimization in diverse MEC cloud networks

In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.

Two-timescale joint service caching and resource allocation for task offloading with edge–cloud cooperation

Task offloading with edge–cloud cooperation has emerged as a pivotal solution for meeting the intricate array of application coupled with dynamically evolving business demand in 6G business scenarios, such as traffic sensing, environmental monitoring, and video surveillance in smart cities. Nonetheless, effectively leveraging heterogeneous edge–cloud network resources for effective task offloading presents substantial challenges. Additionally, the inherent differences in system decision cycles escalate the complexity of the task offloading problem to a new dimension. In this study, we delve into a two-timescale joint service caching and resource allocation optimization for task offloading within edge–cloud cooperation aiming to maximize long-term network performance while adhering to energy constraints. We propose a novel edge–cloud cooperation task offloading scheme that supports both edge–cloud and edge–edge cooperation to effectively balance the edge–cloud and edge–edge loads, promoting the efficient co-utilization of all edge–cloud system resources. Furthermore, we devise an online two-timescale Lyapunov-based joint optimization framework for service caching, task offloading, and computing resource allocation. Our two-timescale decision-making framework can flexibly accommodate the inherent differences in the sensitive decision optimization periods, thereby mitigating the degradation of task offloading performance caused by frequent service caching updates. Finally, theoretical analysis confirms that our proposed algorithm can converge to an approximate optimal solution in polynomial time, and the superiority of our scheme is validated by extensive simulation experiments.

Machine learning-based DDOS attack detection and mitigation in SDNs for IoT environments

Software-defined network (SDN) platforms play a key role in providing security against today's Internet attacks. SDNs decouple the control plane from the data plane to maximize network performance. A DDOS attack is one of several in cloud-based networks. SDNs play a crucial role in controlling DDoS attacks and protecting end nodes like IoT nodes, as well as other computing devices, in large-scale cloud networks. This paper provides an efficient approach to DDoS attack detection and prevention using machine learning algorithms. The paper analyses the performance of SDNs in IoT systems, incorporating a huge set of computing devices that use multi-controllers. It also proposes an effective method to handle DDoS attacks. DDoS attacks are generated from IoT end devices in the infrastructure layer, which targets resources via an SDN-controlled testbed. The proposed ML method outperforms existing methods in terms of accurately and effectively detecting and mitigating flooding DDoS attacks with 99.99% accuracy. The proposed work's results are also compared to the results of other articles to prove the effectiveness of the results.

Network Functions in Cloud: Kubernetes Deployment Challenges

The rapid evolution of cloud computing has paved the way for deploying network functions (NFs) in cloud environments, significantly enhancing the flexibility, scalability, and efficiency of modern network infrastructures. Kubernetes, an open-source container orchestration platform, has emerged as a leading tool for deploying and managing these cloud-based network functions. However, despite its widespread adoption, Kubernetes presents several deployment challenges specific to network functions, stemming from its design, scalability, and operational intricacies. This paper delves into the core challenges faced during the deployment of network functions on Kubernetes, focusing on issues related to network performance, security, service orchestration, and resource management. The abstract aims to provide an overview of the technical hurdles and propose potential strategies to overcome them, thus contributing to the optimization of Kubernetes-based NF deployments in cloud environments. By analyzing existing literature and case studies, the paper identifies key areas where improvements are needed and discusses the implications of these challenges for the future of cloud-based network functions. Ultimately, the paper seeks to guide network architects and cloud engineers in better understanding the complexities of Kubernetes deployments for network functions and in developing more effective strategies for successful implementation.

Bi-level feature Classification Approach in Privacy Preserving Cloud Network for High-Performance Content-based Image retrieval Mechanism

Bi-level feature Classification Approach in Privacy Preserving Cloud Network for High-Performance Content-based Image retrieval Mechanism

Efficient Data Sharing Scheme With Fine-Grained Access Control and Integrity Auditing in Terminal–Edge–Cloud Network

Efficient Data Sharing Scheme With Fine-Grained Access Control and Integrity Auditing in Terminal–Edge–Cloud Network

Efficient message authentication scheme with forward and backward security in Vehicle Cloud Network towards sustainable smart city

The Vehicular Cloud Network (VCN) serves as a crucial component of Intelligent Transportation Systems (ITS), playing an instrumental role in the development of sustainable smart cities. It merges the benefits of both Vehicular Ad Hoc Networks (VANET) and cloud computing. However, VCN faces significant security challenges due to the complexity of communication, the dynamic nature of the environment, and the privacy concerns of data. To address the issues of message security and privacy preservation in VCN, this paper introduces an identity-based key-insulated ring signature (ID-KIRS) scheme for VCN message authentication. Our scheme integrates a secure key update function to address key exposure issues, providing both forward and backward security. The security analysis confirms that our scheme ensures message integrity, unconditional anonymity, and unforgeability. The performance evaluation clearly demonstrates our scheme is efficient, and applicable to vehicle congestion situations, especially car accident scenarios.