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Cloud Computing is popular nowadays due to its storage and data access services. Security and privacy are prime concerns when network threats increase. Cloud computing offers organizations and enterprises a scalable, flexible, and cost-effective infrastructure to store data on the Web. An anomaly-based IDS implementation protects the integrity of the data in a database by identifying and quarantining records when something appears to have changed unexpectedly. Machine learning based clustering and classification methods are used for anomaly based IDS attack classification and scalability in advanced networking environments. Machine learning is a fast, efficient, and adaptable approach to develop intrusion detection models that can deal with emerging threats, i.e., known and unknown attacks (including zero-day attacks). This paper proposes an efficient Hybrid clustering and classification models for implementing an anomaly-based IDS for malicious attack type classifications such as normal (no intrusion), DoS, Probe, U2R, and R2L using threshold-based functions, and the results are tested with two different threshold values (e), 0.01 & 0.5. The experiments have been performed on two tested datasets, namely, NSL-KDD and KDDcup99. Detection rate, False alarm ratio, and accuracy have been used to study the performance of the proposed methodology. After applying the proposed approach, the K-means with random forest has been shown at two different threshold values to have a better classification accuracy, detection rate, and false alarm rate of 99.85%, 99.78% and 0.09% on the NSL-KDD dataset and 98.27%, 98.12% and 2.08% respectively on the KDDcup99 dataset.

The current evolution of ‘cloud neuroscience’ leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

Network segregation is the solution adopted in the IMT-2020 standardization of the International Telecommunications Union (ITU), better known as 5G networks (Fifth Generation Mobile Networks), under development to meet the requirements of performance, reliability, energy, and economic efficiency required by applications in the various verticals of current and near-future economic activities. The philosophy adopted for the IMT-2020 standardization relies on the use of Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Software-Defined Radio (SDR), i.e., the softwarization of the network. Softwarization allows network segregation through its slicing, which is discussed herein this work. Network slicing is performed by a novel Orchestrator, as provided in IMT-2020, which maintains the end-to-end network slices independent of each other and performs horizontal handover when the possibility of a loss of Quality of Service (QoS) is predictively detected by monitoring quality parameters during operation. Therefore, the Orchestrator is dynamic, operates in uptime, and allows horizontal handover. Hence, it chooses the most appropriate telecommunication infrastructure provider and network operator to guarantee QoS and Quality of Experience (QoE) to end-users in each network segment. These features make this work modern and keep it aligned with the actions being carried out by ITU. Based on this objective, as the main result of this paper, we propose an effective architecture for implementing the Orchestrator, not only to contribute to the state of the art for 5G and beyond communication systems but also to generate economic, technological, and social impacts. [ABSTRACT FROM AUTHOR]

Cloud-based image search has emerged as a research focus, but security and privacy concerns limit its application. One secure solution is to encrypt the images before outsourcing them to the cloud. Nevertheless, encryption significantly reduces image availability and suffers from some privacy leakages, such as search pattern leakage, access pattern leakage, and similarity leakage. With the exponential growth of images, the distributed cloud architecture is adopted to provide storage and search services for vast amounts of images. However, ensuring multi-user access control in distributed cloud computing remains a challenge. To address these problems, we propose a multi-user privacy-preserving image search scheme in distributed cloud computing environments. First, we propose an SGX-based privacy-preserving distributed image search framework that leverages SGX to protect image similarity against cloud servers. Second, we present an index re-encryption strategy and an access obfuscation strategy to preserve the search pattern and the access pattern. Last, we propose a multi-secret sharing-empowered multi-user access control method enabling multi-user queries and facilitating lightweight multi-user management in distributed environments. Security analysis and experimental evaluation demonstrate that our proposed scheme guarantees privacy with efficient search. [ABSTRACT FROM AUTHOR]

Nowadays, Software-Defined Networks (SDNs) are increasingly being used in many practical settings, posing a variety of security risks, such as compromised switches. Once a switch is compromised by an attacker, the switch may be either malfunctioning or misconfigured, displaying some abnormal network behaviors, e.g., delaying, dropping, adding, or modifying the traffic. In our previous work, we proposed an efficient scheme for detecting compromised SDN switches based on chaotic analysis of network traffic using an autoregressive-integrated-moving-average model. This scheme showed good results overall; however, it still showed high false-alarm rates due to a hard-set threshold. In this paper, we propose an enhanced scheme to detect compromised SDN switches effectively and reliably. The scheme consists of two phases (online and offline), leveraging the advantages of a stochastic recurrent neural network variant of multivariate time-series-based anomaly detection. Our main idea is to capture the normal patterns of multivariate time series by learning strong representations with the key techniques, such as planar normalizing flow and stochastic variable connection, then reconstruct input data by the representations, and use the reconstruction probabilities to find anomalies. Evaluation results of our proposed scheme yield outstanding performance in comparison with our previous work and other solutions.

The explosive rise of intelligent devices with ubiquitous connectivity have dramatically increased Internet of Things (IoT) traffic in the cloud environment and created potential attack surfaces for cyber-attacks. Traditional security approaches are insufficient and inefficient to address security threats in cloud-based IoT networks. In this vein, software defined networking (SDN), network function virtualization (NFV), and machine learning techniques introduce numerous advantages that can effectively resolve cybersecurity matters for cloud-based IoT systems. In this paper, we propose a collaborative and intelligent network-based intrusion detection system (NIDS) architecture, namely SeArch for SDN-based cloud IoT networks. It composes a hierarchical layer of intelligent IDS nodes working in collaboration to detect anomalies and formulate policy into the SDN-based IoT gateway devices to stop malicious traffic as fast as possible. We first describe a new NIDS architecture with a comprehensive analysis in terms of the system resource and path selection optimizations. Next, the system process logic is extensively investigated through main consecutive procedures, including initialization, runtime operation, and database update. Afterward, we conduct a detailed implementation of the proposed solution in an SDN-based environment and perform a variety of experiments. Finally, evaluation results of the SeArch architecture yield outstanding performance in anomaly detection and mitigation as well as bottleneck problem handling in the SDN-based cloud IoT networks in comparison with existing solutions.

With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminant analysis) were applied to facilitate sub-space analyses. Ancillary environmental and socioeconomic conditions were integrated to support localized model training. Multi-temporal transition probability matrices were deployed in a graph-based Markov–cellular automata simulator to fill in missing data. A comprehensive dataset for Inner Mongolia, China, from 2000 to 2016 was used to assess the feasibility, accuracy, and performance of this gap-filling approach. The result is a cloud-based distributed Markov–cellular automata framework that exploits the scalability and high performance of cloud computing while also achieving high accuracy when filling data gaps common in longer-term LULC studies.

Network segregation is the solution adopted in the IMT-2020 standardization of the International Telecommunications Union (ITU), better known as 5G networks (Fifth Generation Mobile Networks), under development to meet the requirements of performance, reliability, energy, and economic efficiency required by applications in the various verticals of current and near-future economic activities. The philosophy adopted for the IMT-2020 standardization relies on the use of Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Software-Defined Radio (SDR), i.e., the softwarization of the network. Softwarization allows network segregation through its slicing, which is discussed herein this work. Network slicing is performed by a novel Orchestrator, as provided in IMT-2020, which maintains the end-to-end network slices independent of each other and performs horizontal handover when the possibility of a loss of Quality of Service (QoS) is predictively detected by monitoring quality parameters during operation. Therefore, the Orchestrator is dynamic, operates in uptime, and allows horizontal handover. Hence, it chooses the most appropriate telecommunication infrastructure provider and network operator to guarantee QoS and Quality of Experience (QoE) to end-users in each network segment. These features make this work modern and keep it aligned with the actions being carried out by ITU. Based on this objective, as the main result of this paper, we propose an effective architecture for implementing the Orchestrator, not only to contribute to the state of the art for 5G and beyond communication systems but also to generate economic, technological, and social impacts.

In a cloud computing environment, the dynamic resource distribution is especially important for accomplishment of customer demands in a fast and secure manner. A distributed cloud uses fog and edge computing in its infrastructure to reduce the latencies by offering computation and data storage closer to devices. This paper presents the concept of distributed cloud computing and its base components and propose a three-layered architecture model. The study also discusses several platforms which implement the concept in different application areas, such as education, the automotive sector, manufacturing industry and transport. [ABSTRACT FROM AUTHOR]

With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user's requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

The cloud computing paradigm has been defined from several points of view, the main two directions being either as an evolution of the grid and distributed computing paradigm, or, on the contrary, as a disruptive revolution in the classical paradigms of operating systems, network layers and web applications. This paper presents a distributed cloud computing platform called SlapOS, which unifies technologies and communication protocols into a new technology model for offering any application as a service. Both cloud and distributed computing can be efficient methods for optimizing resources that are aggregated from a grid of standard PCs hosted in homes, offices and small data centers. The paper fills a gap in the existing distributed computing literature by providing a distributed cloud computing model which can be applied for deploying various applications.

The increasing momentum of the Internet of Things (IoT) leaded to the development of a huge number of applications in different domains. Those applications are based on different standards and protocols, making therefore the IoT landscape widely fragmented. In this context, the evolution of Web semantic technologies together with the popularity of Cloud computing represents a solution to enable the horizontal integration of various IoT applications and platforms. This is what the Cloud of Things (CoT) aims to achieve. In this paper, we propose the design of a gateway for the Cloud of Things. The proposed gateway is able to manage semantic-like things and at the same time to act as an end-point for the presentation of data to users. Moreover, thanks to the use of virtualized software-which introduces a negligible impact in terms of performance-the gateway enables a lightweight and dense deployment of services. The paper describes the above technologies and how to combine them in order to design the gateway. Furthermore, we provide information about use cases, hardware, performance evaluation, and future hints to enhance the gateway. [ABSTRACT FROM AUTHOR]

The explosive growth of Internet traffic has led to a dramatic increase in demand for data transmission capacity, which has spurred much research activities in real-time logical network construction methods. High-speed services and bulk transmission are now available, and various next-generation converged services are undergoing change as a result of demand by various services. There is also a perceived need now for video-conferencing to exchange know-how between hospitals and medical schools over cloud network. It is now time to elaborate a new wellness service incorporating diverse options. In this paper, we will examine the secure transfer of wellness information over distributed cloud network, especially medical data, and how such information should be disseminated to a network model. [ABSTRACT FROM AUTHOR]

The cloud computing paradigm has been defined from several points of view, the main two directions being either as an evolution of the grid and distributed computing paradigm, or, on the contrary, as a disruptive revolution in the classical paradigms of operating systems, network layers and web applications. This paper presents a distributed cloud computing platform called SlapOS, which unifies technologies and communication protocols into a new technology model for offering any application as a service. Both cloud and distributed computing can be efficient methods for optimizing resources that are aggregated from a grid of standard PCs hosted in homes, offices and small data centers. The paper fills a gap in the existing distributed computing literature by providing a distributed cloud computing model which can be applied for deploying various applications. [ABSTRACT FROM AUTHOR]

With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminant analysis) were applied to facilitate sub-space analyses. Ancillary environmental and socioeconomic conditions were integrated to support localized model training. Multi-temporal transition probability matrices were deployed in a graph-based Markov–cellular automata simulator to fill in missing data. A comprehensive dataset for Inner Mongolia, China, from 2000 to 2016 was used to assess the feasibility, accuracy, and performance of this gap-filling approach. The result is a cloud-based distributed Markov–cellular automata framework that exploits the scalability and high performance of cloud computing while also achieving high accuracy when filling data gaps common in longer-term LULC studies. [ABSTRACT FROM AUTHOR]

SciApps is an open-source, web-based platform for processing, storing, visualizing, and distributing genomic data and analysis results. Built upon the Tapis (formerly Agave) platform, SciApps brings users TB-scale of data storage via CyVerse Data Store and over one million CPUs via the Extreme Science and Engineering Discovery Environment (XSEDE) resources at Texas Advanced Computing Center (TACC). SciApps provides users ways to chain individual jobs into automated and reproducible workflows in a distributed cloud and provides a management system for data, associated metadata, individual analysis jobs, and multi-step workflows. This chapter provides examples of how to (1) submitting, managing, constructing workflows, (2) using public workflows for Bulked Segregant Analysis (BSA), (3) constructing a Data Analysis Center (DAC), and Data Coordination Center (DCC) for the plant ENCODE project., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)