Cloud Computing Environment Research Articles

A Hierarchical Machine Learning Method for Detection and Visualization of Network Intrusions from Big Data

Machine learning is regarded as an effective approach in network intrusion detection, and has gained significant attention in recent studies. However, few intrusion detection methods have been successfully applied to detect anomalies in large-scale network traffic data, and low explainability of the complex algorithms has caused concerns about fairness and accountability. A further problem is that many intrusion detection systems need to work with distributed data sources in the cloud. In this paper, we propose an intrusion detection method based on distributed computing to learn the latent representations from large-scale network data with lower computation time while improving the intrusion detection accuracy. Our proposed classifier, based on a novel hierarchical algorithm combining adaptability and visualization ability from a self-structured unsupervised learning algorithm and achieving explainability from self-explainable supervised algorithms, is able to enhance the understanding of the model and data. The experimental results show that our proposed method is effective, efficient, and scalable in capturing the network traffic patterns and detecting detailed network intrusion information such as type of attack with high detection performance, and is an ideal method to be applied in cloud-computing environments.

LEVERAGING DATA-DRIVEN TECHNIQUES FOR EFFICIENT DATA MINING IN CLOUD COMPUTING ENVIRONMENTS

The capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.

Privacy-Preserving Electric Vehicle Charging Recommendation by Incorporating Full Homomorphic Encryption and Secure Multi-Party Computing

Electric vehicle (EV) charging recommendation can significantly improve global planning performance, corresponding to an increasing risk of privacy leakage. Based on this, this paper investigates the privacy data preservation strategy during the interaction between EVs and charging facilities. It proposes a privacy preservation strategy that aims to ensure EV information security. In a cloud computing environment, users do not want other users and cloud providers to have access to their personal information, which is precisely the problem that secure multi-party computing (SMPC) can solve. At present, full homomorphic encryption (FHE) can solve the problem of user data privacy preservation in cloud computing and big data environments and can realize the whole encryption process. Therefore, a more reasonable charging station selection scheme is provided under the computation of privacy preservation strategies incorporating the FHE-SMPC method. The effectiveness and implementation feasibility of the designed privacy preservation strategy in practical applications is verified through testing and comparative analysis. The results show that the developed strategy can significantly reduce the risk of privacy leakage with limited communication resources and computation time consumption. The results provide new perspectives and methodologies for interactive privacy preservation between EVs and charging stations, with application potential.

Detecting Security System Misconfiguration Threats in Cloud Computing Environments Using AI

Detecting Security System Misconfiguration Threats in Cloud Computing Environments Using AI

Detailed Cloud Linear Regression Services in Cloud Computing Environment

Detailed Cloud Linear Regression Services in Cloud Computing Environment

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.

Dynamic Load Balancing and Service Prioritization Algorithm for Cloud Computing Environments

Bridge infrastructure plays a critical role in ensuring the safe and efficient transportation of goods and people. This research paper investigates the strength characteristics of bridges with a focus on understanding the factors influencing their structural integrity and resilience. The study employs a comprehensive methodology that includes field inspections, laboratory testing, and advanced computational modeling techniques to assess the performance of various bridge types under different loading conditions. Findings reveal the complex interplay between design parameters, material properties, and environmental factors in determining bridge strength and durability. Moreover, the research highlights the importance of proactive maintenance strategies and innovative engineering solutions to enhance the resilience of bridge infrastructure against aging, deterioration, and extreme events. The insights gained from this study have significant implications for bridge design, maintenance practices, and public safety, ultimately contributing to the sustainability and reliability of transportation networks.

Advancing Volcanic Activity Monitoring: A Near-Real-Time Approach with Remote Sensing Data Fusion for Radiative Power Estimation

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity.

A Critical Review and Analysis of Load Balancing Methods in Cloud Computing Environment

A Critical Review and Analysis of Load Balancing Methods in Cloud Computing Environment

A Risk Assessment Framework for Mobile Apps in Mobile Cloud Computing Environments

A Risk Assessment Framework for Mobile Apps in Mobile Cloud Computing Environments

Securing Technology for Virtual Collaborations and Learning Process Using Cloud Computing Environment

Securing Technology for Virtual Collaborations and Learning Process Using Cloud Computing Environment

Efficient Heuristic Management of Cloud Data Lakes for Enhanced Big Data Handling in Cloud Computing Environments

Efficient control of cloud data lakes is important for improving big data dealing in cloud computing settings. Cloud data lakes serve as stores for vast amounts of organized, semi-structured, and unstructured data from varied sources, offering scaling and freedom in data storage and processing. However, the sheer amount and range of data offer difficulties in improving storage, access, and analysis processes. This study suggests a heuristic method to handle these issues by improving the order and recovery of data within cloud data lakes. The heuristic method focuses on automatically handling data splitting, storage, and caching strategies based on data access trends and task factors. By leveraging heuristic principles, such as adaptable splitting and predictive caching, the suggested method aims to reduce delay and improve total query speed. Key components of the heuristic management include adaptable splitting methods that actively change data partition sizes based on access frequency and data distribution patterns. This flexible method ensures that frequently accessed data is easily available, while less accessed data is efficiently saved to improve storage usage. Furthermore, predictive storing methods expect future data access patterns using machine learning models or previous data trends. By strategically saving relevant datasets in memory or high-speed storage, the system reduces delay for repeated questions, thereby improving real-time data processing capabilities. The success of the suggested heuristic management method is tested through models and benchmarks using real big data tasks. Comparative research against standard static splitting and caching methods shows significant changes in query response times and resource utilization efficiency.

Analyzing Privacy and Security in Cloud Computing Environments

With the rapid surge of spam across the Internet and its various forms, effectively identifying and combating spam has become an urgent priority. Cloud computing offers significant advantages in terms of storage and processing capabilities, making it a viable solution for analysing vast amounts of email data. To address the dynamic nature of spam and its life cycle, an anti-spam system with feedback reassessment is proposed. This system incorporates a text filtering approach based on active learning, involving four key stages: training, filtering, feedback, and re-filtering. Compared to traditional systems, the feedback-enabled filtering system demonstrates improved keyword filtering. To further enhance the accuracy of spam detection and minimize misjudgements in legitimate emails, leveraging weighted decision-making based on email header information is recommended. Additionally, for emails with sparse content, employing title weighting in the filtering process proves to be both feasible and effective, particularly in identifying spam with minimal text content. Given the advancements of cloud-based filtering methods over traditional algorithms, leveraging cloud computing holds promise in effectively combating the escalating volume of spam. As such, this paper delves into an in-depth exploration of spam identification within cloud computing environments, focusing on text filtering systems. This study is informed by a comprehensive analysis of existing anti-spam technologies, aiming to contribute to the ongoing efforts in mitigating spam proliferation online.

Optimizing Software Development Processes in Cloud Computing Environments Using Agile Methodologies and DevOps Practices

This exploration paper investigates the enhancement of programming improvement processes inside distributed computing conditions by coordinating Spry philosophies and DevOps rehearses. Distributed computing offers adaptable, on-request assets that altogether upgrade programming improvement and arrangement productivity. Nonetheless, expanding these advantages requires versatile strategies and constant incorporation and conveyance (CI/Album) rehearses. The review analyzes how Deft standards, with their emphasis on iterative turn of events and coordinated effort persistent criticism. Through contextual analyses and experimental examination, the exploration features best practices for executing Light-footed and DevOps in cloud-based projects. The discoveries give noteworthy bits of knowledge to programming improvement groups planning to use cloud advances to their maximum capacity.

An Enhanced Model for Intrusion Detection in a Cloud Computing Environment

Intrusion is an important issue in computer networks especially in cloud computing where all the services are served using the internet. The fully distributed and open structure of cloud computing and services has made it an even more attractive target for potential intruders. The more sophisticated hackers and attackers get, the more there is work for the defense to prevent such attacks. A cloud computing system can be exposed to threats which include the integrity, confidentiality, and availability of its resources, its data, and the virtualized infrastructure can be vulnerable. The problem becomes bigger when an internal intruder misuses a cloud with massive computing power and storage capacity as a malicious party. This research developed an enhanced model for intrusion detection that monitors and analyzes data in a cloud environment and detects intrusion in the system or network. The model can detect intrusions from external and malicious internal (authorized and unauthorized) users by normalizing and classifying all data packets using machine learning techniques. The developed system is an enhanced model of Zhang by combining it with two machine learning techniques: Support vector machine and Bayesian network to aid in the classification of normal data and intrusion data to detect intrusions. The developed model is evaluated and found to be able to make strong predictions, detect attacks, and still maintain the efficiency of the network. The system, when implemented, can detect intruders by classification of data packets and also improve the existing system in terms of providing more accurate and more efficient intrusion detection. It also provides worthwhile information about malicious network traffic, helping to identify the source of the incoming probes or attacks, collecting forensic evidence that can be used to identify intruders, and alerting security personnel that a network invasion may be in progress.

Research on Intelligent Cybersecurity Protection System in Cloud Computing Environment

In the digital era, cloud computing has transformed data processing, storage, and analysis with its scalability and flexibility. However, it also introduces new security challenges, such as data breaches and unauthorized access, which are exacerbated by Advanced Persistent Threats (APT) and zero-day vulnerabilities. This study aims to construct an intelligent network security protection system that adapts to the dynamic cloud computing environment, leveraging artificial intelligence and machine learning to enhance security efficiency and accuracy. The research objectives include developing a system that maintains privacy protection and user experience while ensuring secure access to cloud services. The paper presents a multi-layered architecture for the intelligent cybersecurity protection system, incorporating data collection, threat detection, access control, and adaptive security policies. Experimental methods and results validate the system’s effectiveness, demonstrating improved detection rates and reduced false positives compared to existing solutions. The study concludes with a discussion on the model’s advantages, limitations, and future application prospects in the field of cloud computing security.

Learning to Optimize Workflow Scheduling for an Edge–Cloud Computing Environment

Learning to Optimize Workflow Scheduling for an Edge–Cloud Computing Environment

Hybrid fault tolerant cost aware mechanism for scientific workflow in cloud computing

Cloud computing provides solutions for diverse commercial and academic applications which is the primary goal. Scientific workflows are used in the cloud-computing environment to analyses large-scale scientific applications. For scientific workflows, many data is required, and a single scientific workflow that includes hundreds of stages, depending on the application's time restrictions, task failures, money limits, incorrect task organization, and task management issues can all hinder the implementation of scientific methods. In light of this, a cloud-based scientific workflow management and scheduling system that is fault-tolerant and data-oriented method are proposed. This research designs a novel hybrid cost-aware fault tolerant (HCFT) mechanism for minimizing the cost. Moreover, HCFT integrates optimal clustering and efficient resource utilization through parallel and distributed execution. Novelty of HCFT lies in novel clustering of the similar task for improvisation, CyberShake, laser interferometer gravitational wave observatory (LIGO), Montage, and sRNA identification protocol using high throughput technology (SIPHT) processes are used in the simulations to evaluate the performance of the proposed approach.

Dynamic Load Balancing in Cloud Computing Environments: A Genetic Algorithm Approach

Dynamic Load Balancing in Cloud Computing Environments: A Genetic Algorithm Approach

A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments

Cloud computing services have become indispensable to people’s lives. Many of their activities are performed through cloud services, from small companies to large enterprises and individuals to government agencies. It has enabled clients to use companies’ services on demand at the lowest cost anywhere, anytime, over the Internet. Despite these advantages, cloud networks are vulnerable to many types of attacks. However, as the adoption of cloud services accelerates, the risks associated with these services have also increased. For this reason, solutions have been implemented to improve cloud security, such as monitoring networks, the backbone of the cloud infrastructure, and detecting and classifying cyberattacks. Therefore, an intrusion detection system (IDS) is one of the essential defenses for detecting attacks in the cloud computing network. Current IDSs encounter some challenges in handling and simultaneously analyzing the large scale of traffic found in the cloud environment, and this affects the accuracy of cyberattack detection. Therefore, this research proposes a deep learning-based model by leveraging advanced convolutional neural networks (CNNs)-based model architecture to detect cyberattacks in the cloud environment efficiently. The proposed CNN-based model for intrusion detection consists of multiple significant stages: dataset collection, preprocessing, the SMOTE balance data strategy, feature selection, model training, testing, and performance evaluation. Experiments have demonstrated that the proposed model is highly effective in protecting cloud networks against various potential attacks. With over 98.67% accuracy, precision, and recall, the model has proven its ability to detect and classify network intrusions. Detailed analyses show that the model is proficient in securing cloud security measures and mitigating the risks associated with evolving security threats.