Biblio

In Cloud Computing, a wide range of virtual platforms are integrated and offer users a flexible pay-as-you-need service. Compared to conventional computing systems, the provision of an acceptable degree of resilience to cloud services is a daunting challenge due to the complexities of the cloud environment and the need for efficient technology that could sustain cloud advantages over other technologies. For a cloud guest resilience service solution, we provide architectural design, installation specifics, and performance outcomes throughout this article. Virtual Machine Manager (VMM) enables execution statistical test of the virtual machine states to be monitored and avoids to reach faulty states.

There are problems in which the use of linear regression is not sufficiently justified. In these cases, fuzzy linear regression can be used as a modeling tool. The problem of constructing a fuzzy linear regression can usually be reduced to a linear programming problem. One of the features of the resulting linear programming problem is that it uses a relatively large number of constraints in the form of inequalities with a relatively small number of variables. It is known that the problem of constructing a fuzzy linear regression is reduced to the problem of linear programming. If the user does not have enough computing power the resulting problem can be transferred to the cloud server. Two approaches are used for the confidential transfer of the problem to the server: the approach based on cryptographic encryption, and the transformational approach. The paper describes a protocol based on the transformational approach that allows for secure outsourcing of fuzzy linear regression.

In this paper, we investigate how to automatically persist versioned data structures in distributed settings (e.g. cloud + edge) using append-only storage. By doing so, we facilitate resiliency by enabling program state to survive program activations and termination, and program-level data structures and their version information to be accessed programmatically by multiple clients (for replay, provenance tracking, debugging, and coordination avoidance, and more). These features are useful in distributed, failure-prone contexts such as those for heterogeneous and pervasive Internet of Things (IoT) deployments. We prototype our approach within an open-source, distributed operating system for IoT. Our results show that it is possible to achieve algorithmic complexities similar to those of in-memory versioning but in a distributed setting.

The randomness, ambiguity and some other uncertainties of trust relationships in Wireless Sensor Networks (WSNs) make existing trust management methods often unsatisfactory in terms of accuracy. This paper proposes a trust evaluation method based on cloud model for malicious node detection. The conversion between qualitative and quantitative sensor node trust degree is achieved. Firstly, nodes cooperate with each other to establish a standard cloud template for malicious nodes and a standard cloud template for normal nodes, so that malicious nodes have a qualitative description to be either malicious or normal. Secondly, the trust cloud template obtained during the interactions is matched against the previous standard templates to achieve the detection of malicious nodes. Simulation results demonstrate that the proposed method greatly improves the accuracy of malicious nodes detection.

Recently, Mobile Edge Cloud computing (MEC) has attracted attention both from academia and industry. The idea of moving a part of cloud resources closer to users and data sources can bring many advantages in terms of speed, data traffic, security and context-aware services. The MEC infrastructure does not only host and serves applications next to the end-users, but services can be dynamically migrated and reallocated as mobile users move in order to guarantee latency and performance constraints. This specific requirement calls for the involvement and collaboration of multiple MEC providers, which raises a major issue related to trustworthiness. Two main challenges need to be addressed: i) trustworthiness needs to be handled in a manner that does not affect latency or performance, ii) trustworthiness is considered in different dimensions - not only security metrics but also performance and quality metrics in general. In this paper, we propose a trust layer for public MEC infrastructure that handles establishing and updating trust relations among all MEC entities, making the interaction withing a MEC network transparent. First, we define trust attributes affecting the trusted quality of the entire infrastructure and then a methodology with a computation model that combines these trust attribute values. Our experiments showed that the trust model allows us to reduce latency by removing the burden from a single MEC node, while at the same time increase the network trustworthiness.

The burgeoning networked computing devices create many distributed systems and generate new signals on a large scale. Many Internet of Things (IoT) applications, such as peer-to-peer streaming of multimedia data, crowdsourcing, and measurement by sensor networks, can be modeled as a form of big data. Processing massive data calls for new data structures and algorithms different from traditional ones designed for small-scale problems. For measurement from networked distributed systems, we consider an essential data format: signals on graphs. Due to limited computing resources, the sensor nodes in the distributed systems may outsource the computing tasks to third parties, such as cloud platforms, arising a severe concern on data privacy. A de-facto solution is to have third parties only process encrypted data. We propose a novel and efficient privacy-preserving secure outsourced computation protocol for denoising signals on the graph based on the information-theoretic secure multi-party computation (ITS-MPC). Denoising the data makes paths for further meaningful data processing. From experimenting with our algorithms in a testbed, the results indicate a better efficiency of our approach than a counterpart approach with computational security.

As an important part of the intelligent measurement system, IOT terminal is in the “edge” layer of the intelligent measurement system architecture. It is the key node of power grid management and cloud fog integration. Its information security is the key to the construction of the security system of intelligent measurement, and the security link between the cloud and sensor measurement. With the in-depth integration of energy flow, information flow and business flow, and the in-depth application of digital technologies such as cloud computing, big data, internet of things, mobile Internet and artificial intelligence, the transformation and development of power system to digital and high-quality digital power grid has been accelerated. As a typical multi-dimensional complex system combining physical space and information space, the security threats and risks faced by the digital grid are more complex. The security risks in the information space will transfer the hazards to the power system and physical space. The Internet of things terminal is facing a more complex situation in the security field than before. This paper studies the feasibility of the security monitoring technology of the Internet of things terminal, in order to reduce the potential risks, improve the safe operation environment of the Internet of things terminal and improve the level of the security protection of the Internet of things terminal. One is to study the potential security problems of Internet of things terminal, and put forward the technical specification of security protection of Internet of things terminal. The second is to study the Internet of things terminal security detection technology, research and develop terminal security detection platform, and realize the unified detection of terminal security protection. The third is to study the security monitoring technology of the Internet of things terminal, develop the security monitoring system of the Internet of things terminal, realize the terminal security situation awareness and threat identification, timely discover the terminal security vulnerabilities, and ensure the stable and safe operation of the terminal and related business master station.

With the continuous emergence of cloud computing technology, cloud infrastructure software will become the mainstream application model in the future. Among the databases, relational databases occupy the largest market share. Therefore, the relational cloud database will be the main product of the combination of database technology and cloud computing technology, and will become an important branch of the database industry. This article explores the establishment of an evaluation system framework for relational databases, helping enterprises to select relational cloud database products according to a clear goal and path. This article can help enterprises complete the landing of relational cloud database projects.

Cloud computing has changed the paradigm of using computing resources. It has shifted from traditional storage and computing to Internet based computing leveraging economy of scale, cost saving, elimination of data redundancy, scalability, availability and regulatory compliance. With these, cloud also brings plenty of security issues. As security is not a one-time solution, there have been efforts to investigate and provide countermeasures. In the wake of emerging quantum computers, the aim of post-quantum cryptography is to develop cryptography schemes that are secure against both classical computers and quantum computers. Since cloud is widely used across the globe for outsourcing data, it is essential to strive at providing betterment of security schemes from time to time. This paper reviews recent development, methods of cloud data security in post-quantum perspectives. It provides useful insights pertaining to the security schemes used to safeguard data dynamics associated with cloud computing. The findings of this paper gives directions for further research in pursuit of more secure cloud data storage and retrieval.

The development of edge computing and machine learning technologies have led to the growth of Industrial IoT systems. Autonomous decision making and smart manufacturing are flourishing in the current age of Industry 4.0. By providing more compute power to edge devices and connecting them to the internet, the so-called Cyber Physical Systems are prone to security threats like never before. Security in the current industry is based on cryptographic techniques that use pseudorandom number keys. Keys generated by a pseudo-random number generator pose a security threat as they can be predicted by a malicious third party. In this work, we propose a secure Industrial IoT Architecture that makes use of true random numbers generated by a quantum random number generator (QRNG). CITRIOT's FireConnect IoT node is used to show the proof of concept in a quantum-safe network where the random keys are generated by a cloud based quantum device. We provide an implementation of QRNG on both real quantum computer and quantum simulator. Then, we compare the results with pseudorandom numbers generated by a classical computer.

At present, in the face of the huge and complex data in cloud computing, the parallel computing ability of quantum computing is particularly important. Quantum principal component analysis algorithm is used as a method of quantum state tomography. We perform feature extraction on the eigenvalue matrix of the density matrix after feature decomposition to achieve dimensionality reduction, proposed quantum principal component extraction algorithm (QPCE). Compared with the classic algorithm, this algorithm achieves an exponential speedup under certain conditions. The specific realization of the quantum circuit is given. And considering the limited computing power of the client, we propose a quantum homomorphic ciphertext dimension reduction scheme (QHEDR), the client can encrypt the quantum data and upload it to the cloud for computing. And through the quantum homomorphic encryption scheme to ensure security. After the calculation is completed, the client updates the key locally and decrypts the ciphertext result. We have implemented a quantum ciphertext dimensionality reduction scheme implemented in the quantum cloud, which does not require interaction and ensures safety. In addition, we have carried out experimental verification on the QPCE algorithm on IBM's real computing platform. Experimental results show that the algorithm can perform ciphertext dimension reduction safely and effectively.

There is an increasing trend in cloud adoption of enterprise applications in, for example, manufacturing, healthcare, and finance. Such applications are routinely subject to targeted cyberattacks, which result in significant loss of sensitive data (e.g., due to data exfiltration in advanced persistent threats) or valuable utilities (e.g., due to resource the exfiltration of power in cryptojacking). There is a critical need to train highly skilled cybersecurity professionals, who are capable of defending against such targeted attacks. In this article, we present the design, development, and evaluation of the Mizzou Cyber Range, an online platform to learn basic/advanced cyber defense concepts and perform training exercises to engender the next-generation cybersecurity workforce. Mizzou Cyber Range features flexibility, scalability, portability, and extendability in delivering cyberattack/defense learning modules to students. We detail our “research-inspired learning” and “learn-apply-create” three-phase pedagogy methodologies in the development of four learning modules that include laboratory exercises and self-study activities using realistic cloud-based application testbeds. The learning modules allow students to gain skills in using latest technologies (e.g., elastic capacity provisioning, software-defined everything infrastructure) to implement sophisticated “attack defense by pretense” techniques. Students can also use the learning modules to understand the attacker-defender game in order to create disincentives (i.e., pretense initiation) that make the attacker's tasks more difficult, costly, time consuming, and uncertain. Lastly, we show the benefits of our Mizzou Cyber Range through the evaluation of student learning using auto-grading, rank assessments with peer standing, and monitoring of students' performance via feedback from prelab evaluation surveys and postlab technical assessments.

Real time data analysis in the context of e.g. realtime monitoring or computational steering is an important tool in many fields of science, allowing scientists to make the best use of limited resources such as sensors and HPC platforms. These tools typically rely on large amounts of continuously collected data that needs to be processed in near-real time to avoid wasting compute, storage, and networking resources. Streaming pipelines are a natural fit for this use case but are inconvenient to use on high-performance computing (HPC) systems because of the diverging system software environment with big data, increasing both the cost and the complexity of the solution. In this paper we propose Neon, a clean-slate design of a streaming data processing framework for HPC systems that enables users to create arbitrarily large streaming pipelines. The experimental results on the Bebop supercomputer show significant performance improvements compared with Apache Storm, with up to 2x increased throughput and reduced latency.

Network slicing qualitatively transforms network infrastructures such that they have maximum flexibility in the context of ever-changing service requirements. While the agility of cloud native network functions (CNFs) demonstrates significant promise, virtualization and softwarization severely degrade the performance of such network functions. Considerable efforts were expended to improve the performance of virtualized systems, and at this stage 10 Gbps throughput is a real target even for container/VM-based applications. Nonetheless, the current performance of CNFs with state-of-the-art enhancements does not meet the performance requirements of next-generation 6G networks that aim for terabit-class throughput. The present pace of performance enhancements in hardware indicates that straightforward optimization of existing system components has limited possibility of filling the performance gap. As it would be reasonable to expect a single silver-bullet technology to dramatically enhance the ability of CNFs, an organic integration of various data-plane technologies with a comprehensive vision is a potential approach. In this paper, we show a future vision of system architecture for terabit-class CNFs based on effective harmonization of the technologies within the wide-range of network systems consisting of commodity hardware devices. We focus not only on the performance aspect of CNFs but also other pragmatic aspects such as interoperability with the current environment (not clean slate). We also highlight the remaining missing-link technologies revealed by the goal-oriented approach.

In this paper, we present the Cloud Property Graph (CloudPG), which bridges the gap between static code analysis and runtime security assessment of cloud services. The CloudPG is able to resolve data flows between cloud applications deployed on different resources, and contextualizes the graph with runtime information, such as encryption settings. To provide a vendorand technology-independent representation of a cloud service's security posture, the graph is based on an ontology of cloud resources, their functionalities and security features. We show, using an example, that our CloudPG framework can be used by security experts to identify weaknesses in their cloud deployments, spanning multiple vendors or technologies, such as AWS, Azure and Kubernetes. This includes misconfigurations, such as publicly accessible storages or undesired data flows within a cloud service, as restricted by regulations such as GDPR.

Reversible data hiding (RDH) is an emerging area in the field of information security. The RDH schemes are widely explored in the field of cloud computing for data authentication and in medical image transmission for clinical data transmission along with medical images. The RDH schemes allow the data hider to embed sensitive information in digital content in such a way that later it can be extracted while recovering the original image. In this research, we explored the use of the RDH through the encryption scheme in a biometric authentication system. The internet of things (IoT) enabled biometric authentication systems are very common nowadays. In general, in biometric authentication, computationally complex tasks such as feature extraction and feature matching will be performed in a cloud server. The user-side devices will capture biometric data such as the face, fingerprint, or iris and it will be directly communicated to the cloud server for further processing. Since the confidentiality of biometric data needs to be maintained during the transmission, the original biometric data will be encrypted using any one of the data encryption techniques. In this manuscript, we propose the use of RDH through encryption approach to transmit two different biometric data as a single file without compromising confidentiality. The proposed scheme will ensure the integrity of the biometric data during transmission. For data hiding purposes, we have used a block-wise RDH through encryption scheme. The experimental study of the proposed scheme is carried out by embedding fingerprint data in the face images. The validation of the proposed scheme is carried out by extracting the fingerprint details from the face images during image decryption. The scheme ensures the exact recovery of face image images and fingerprint data at the receiver site.

This study shows the effectiveness of anomaly-based IDS using long short-term memory(LSTM) based on the newly developed dataset called UNSW-NB15 while considering root mean square error and mean absolute error as evaluation metrics for accuracy. For each attack, 80% and 90% of samples were used as LSTM inputs and trained this model while increasing epoch values. Furthermore, this model has predicted attack points by applying test data and produced possible attack points for each attack at the 3rd time frame against the actual attack point. However, in the case of an Exploit attack, the consecutive overlapping attacks happen, there was ambiguity in the interpretation of the numerical values calculated by the LSTM. We presented a methodology for training data with binary values using LSTM and evaluation with RMSE metrics throughout this study.

Cloud computing, supported by advancements in virtualisation and distributed computing, became the default options for implementing the IT infrastructure of organisations. Medical data and in particular medical images have increasing storage space and remote access requirements. Cloud computing satisfies these requirements but unclear safeguards on data security can expose sensitive data to possible attacks. Furthermore, recent changes in legislation imposed additional security constraints in technology to ensure the privacy of individuals and the integrity of data when stored in the cloud. In contrast with this trend, current data security methods, based on encryption, create an additional overhead to the performance, and often they are not allowed in public cloud servers. Hence, this paper proposes a mechanism that combines data fragmentation to protect medical images on the public cloud servers, and a NoSQL database to secure an efficient organisation of such data. Results of this paper indicate that the latency of the proposed method is significantly lower if compared with AES, one of the most adopted data encryption mechanisms. Therefore, the proposed method is an optimal trade-off in environments with low latency requirements or limited resources.

Recently, as a result of the COVID-19 pandemic, the internet service has seen an upsurge in use. As a result, the usage of cloud computing apps, which offer services to end users on a subscription basis, rises in this situation. However, the availability and efficiency of cloud computing resources are impacted by DDoS attacks, which are designed to disrupt the availability and processing power of cloud computing services. Because there is no effective way for detecting or filtering DDoS attacks, they are a dependable weapon for cyber-attackers. Recently, researchers have been experimenting with machine learning (ML) methods in order to create efficient machine learning-based strategies for detecting DDoS assaults. In this context, we propose a technique for detecting DDoS attacks in a cloud computing environment using big data and deep learning algorithms. The proposed technique utilises big data spark technology to analyse a large number of incoming packets and a deep learning machine learning algorithm to filter malicious packets. The KDDCUP99 dataset was used for training and testing, and an accuracy of 99.73% was achieved.

Data and veracious information are an important feature of any organization; it takes special care as a like asset of the organization. Cloud computing system main target to provide service to the user like high-speed access user data for storage and retrieval. Now, big concern is data protection in cloud computing technology as because data leaking and various malicious attacks happened in cloud computing technology. This study provides user data protection in the cloud storage device. The article presents the architecture of a data security hybrid infrastructure that protects and stores the user data from the unauthenticated user. In this hybrid model, we use a different type of security model.

Cloud computing has become ubiquitous in the modern world and has offered a number of promising and transformative technological opportunities. However, organizations that use cloud platforms are also concerned about cloud security and new threats that arise due to cloud adoption. Digital forensic investigations (DFI) are undertaken when a security incident (i.e., successful attack) has been identified. Forensics data collection is an integral part of DFIs. This paper presents results from a survey of existing literature on challenges related to forensics data collection in cloud. A taxonomy of major challenges was developed to help organizations understand and thus better prepare for forensics data collection.

The rapid development of blockchain application technology promotes continuous exploration in the field of computer application science. Although it is still in the initial stage of development, the technical features of blockchain technology such as decentralization, identity verification, tamper resistance, data integrity, and security are regarded as excellent solutions to today's computer security technical problems. In this paper, we will analyze and compare blockchain data storage and cloud data processing technologies, focusing on the concept and technology of blockchain distributed data storage technology, and analyze and summarize the key issues. The results of this paper will provide a useful reference for the application and research of blockchain technology in cloud storage security.

Big data emerges as an important technology that addresses the storage, processing and analytics aspects of massive data characterized by 5V's (volume, velocity, variety, veracity, value) which has grown exponentially beyond the handling capacity traditional data architectures. The most significant technologies include the parallel storage and processing framework which requires entirely new IT infrastructures to facilitate big data adoption. Cloud computing emerges as a successful paradigm in computing technology that shifted the business landscape of IT infrastructures towards service-oriented basis. Cloud service providers build IT infrastructures and technologies and offer them as services which can be accessed through internet to the consumers. This paper discusses on the facilitating role of cloud computing in the field of big data analytics. Cloud deployment models concerning the architectural aspect and the current trend of adoption are introduced. The fundamental cloud services models concerning the infrastructural and technological provisioning are introduced while the emerging cloud services models related to big data are discussed with examples of technology platforms offered by the big cloud service providers - Amazon, Google, Microsoft and Cloudera. The main advantages of cloud adoption in terms of availability and scalability for big data are reiterated. Lastly, the challenges concerning cloud security, data privacy and data governance of consuming and adopting big data in the cloud are highlighted.

In view of the continuous progress of current science and technology, cloud computing has been widely used in various fields. This paper proposes a secure data storage architecture based on cloud computing. The architecture studies the security issues of cloud computing from two aspects: data storage and data security, and proposes a data storage mode based on Cache and a data security mode based on third-party authentication, thereby improving the availability of data, from data storage to transmission. Corresponding protection measures have been established to realize effective protection of cloud data.

In the big data cloud computing environment, data security issues have become a focus of attention. This paper delivers an overview of conceptions, characteristics and advanced technologies for big data cloud computing. Security issues of data quality and privacy control are elaborated pertaining to data access, data isolation, data integrity, data destruction, data transmission and data sharing. Eventually, a virtualization architecture and related strategies are proposed to against threats and enhance the data security in big data cloud environment.