Intrusion detection system (IDS) provides an important basis for the network defense. Due to the development of the cloud computing and social network, massive amounts of data are generated, which inevitably brings much pressure to IDS. And therefore, it becomes crucial to efficiently divide the data into different classes over big data according to data features. Moreover, we can further determine whether one is normal behavior or not based on the classes information. Although the clustering approach based on $K$ -means for IDS has been well studied, unfortunately directly using it in big data environment may suffer from inappropriateness. On the one hand, the efficiency of data clustering needs to be improved. On the other hand, differ from the classification, there is no unified evaluation indicator for clustering issue, and thus, it is necessary to study which indicator is more suitable for evaluating the clustering results of IDS. In this paper, we propose a clustering method for IDS based on Mini Batch $K$ -means combined with principal component analysis. First, a preprocessing method is proposed to digitize the strings and then the data set is normalized so as to improve the clustering efficiency. Second, the principal component analysis method is used to reduce the dimension of the processed data set aiming to further improve the clustering efficiency, and then mini batch $K$ -means method is used for data clustering. More specifically, we use $K$ -means++ to initialize the centers of cluster in order to avoid the algorithm getting into the local optimum, in addition, we choose the Calsski Harabasz indicator so that the clustering result is more easily determined. Compared with the other methods, the experimental results and the time complexity analysis show that our proposed method is effective and efficient. Above all, our proposed clustering method can be used for IDS over big data environment.

Clustering Approach Based on Mini Batch Kmeans for Intrusion Detection System Over Big Data

Recommender systems are a promising way for users to quickly find the valuable information that they are interested in from massive data. Concretely, by capturing the user's personalized preferences, a recommender system can return a list of recommended items that best match the user preferences by using collaborative filtering. However, in the big data environment, the heavily fragmented distribution of the QoS (Quality of Services) data for recommendation decision- making presents a large challenge when integrating the QoS data from different platforms while ensuring that the sensitive user information contained in the QoS data is secure. Furthermore, due to the common tradeoff between data availability and privacy in data-driven applications, protecting the sensitive user information contained in the QoS data will probably decrease the availability of QoS data and finally produce inaccurate recommendation results. Considering these challenges, we enhance the classic Locality-Sensitive Hashing (LSH) technique, after which we propose an approach based on enhanced LSH for accurate and less-sensitive cross-platform recommendation decision-makings. Finally, extensive experiments are designed and tested on the reputable WS-DREAM dataset. The test reports prove the benefits of our work compared to other competitive approaches in the aspects of recommendation accuracy, efficiency and privacy protection performances.

Privacy-Aware Cross-Platform Service Recommendation Based on Enhanced Locality-Sensitive Hashing

It is the overriding trend of the present-day world that traditional systems and mobile devices are currently transforming into intelligent systems and smart devices. Against this backdrop, cyber-physical systems (CPSs) and Internet-of-Things (IoT) emerge as the times require. To achieve the parallel interactions between the human world and the computer network, IoT along with wireless mobile communication and computing open up some future opportunities as well as challenges for constructing a novel cyber-physical-social system (CPSS) that takes human factors into account during the system operation and management. In this article, a brief comprehensive survey is provided on some of the current research work that contributes to enabling CPSSs. Some crucial aspects of CPSSs are identified, including: the development from CPSs to CPSSs, architecture design, applications, standards, real-world case studies, enabling techniques and networks for CPSSs. To lay a foundation for the development of the upcoming smart world, we further propose a virtualization architecture and an integrated framework of caching, computing and networking for CPSSs. Simulations verify the performance improvement of the proposals. At last, some research issues with challenges and possible solutions are unearthed for researchers in the related research areas.

Cyber-Physical-Social Systems: A State-of-the-Art Survey, Challenges and Opportunities

Industry 5.0: Prospect and retrospect

Future wireless networks will provide high-bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from virtual reality to the Internet of Things. To this aim, edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 improves the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bit-rate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined.

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Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

Cyber-physical-social systems comprise physical, cyber, and social worlds with various resources as services. The operation and configuration of these services require service composition approaches that can be used to integrate essential components in these worlds, and organize and share relevant information as needed. Although the present service-oriented architecture can effectively support service composition, there still exist many challenges such as high time cost and low reliability for cyber-physical-social systems. In this paper, we propose a fast and reliable service composition approach to integrate the physical network, cyberspace, and social network. In this approach, first, skyline component computation is performed to reduce the solution space; then, the coefficient of variation is employed to filter the components with high quality of service (QoS) fluctuation and finally the best optimal components are selected by maximizing the fitness function based on users’ end-to-end QoS requirements. We applied our approach to real-world dataset and the results showed that our proposed approach has better reliability as well as lower time cost than other approaches.

Service Composition in Cyber-Physical-Social Systems

With the development of converged all-IP networks, new network services—supporting, among others, image, audio, and video—with high diversity emerge, which brings huge pressure and fierce competition to service providers because of increasing choices for users Assessing end user’s quality of experience (QoE) has become one of the main issues to meet high user requirements and satisfaction under any circumstances, which means receiving any service, anytime, anywhere, on any device, by any media and networking technology, across any operator domain In this paper, we provide an overview of the field of QoE assessment approaches including three aspects: subjective assessment, objective assessment, and hybrid assessment The discussion includes the definition, influence factors, and quantification approaches for QoE Then, these three assessment methodologies are surveyed and compared across a number of factors This paper also introduces challenges and opportunities in the area of QoE assessment

Survey on QoE Assessment Approach for Network Service

Edge computing has become an important innovative business model in the 5G era, especially its low latency feature, which is considered to be not available in traditional solutions Therefore, edge computing can provide more service capabilities and has a wider range of application scenarios However, the collaboration of computing power between edge computing and cloud computing in the central position has become a new technical problem That is, it is necessary to achieve cloud-to-network collaboration, cloud-to-edge collaboration, or even edge-to-edge collaboration among edge computing, cloud computing, and network to achieve the optimisation of resource utilisation On the basis of studying the compute distribution and scheduling requirements of edge computing, this paper introduces a compute network scheme based on the deep fusion of cloud, edge and network, called compute first networking (CFN) Firstly, we introduce the basic concept and related work of CFN Then, we mainly discuss the framework and key technology of CFN After that, we present some applications with respect to CFN Finally, we discuss the challenges and opportunities in the area of compute first networking

An overview of compute first networking

Survey on tracking and registration technology for mobile augmented reality

In order to characterize complex higher-order interactions among variables in a system, we introduce a new framework for decomposing the information entropy of variables in a system, termed System Information Decomposition (SID). Diverging from Partial Information Decomposition (PID) correlation methods, which quantify the interaction between a single target variable and a collection of source variables, SID extends those approaches by equally examining the interactions among all system variables. Specifically, we establish the robustness of the SID framework by proving all the information atoms are symmetric, which detaches the unique, redundant, and synergistic information from the specific target variable, empowering them to describe the relationship among variables. Additionally, we analyze the relationship between SID and existing information measures and propose several properties that SID quantitative methods should follow. Furthermore, by employing an illustrative example, we demonstrate that SID uncovers a higher-order interaction relationships among variables that cannot be captured by current measures of probability and information and provide two approximate calculation methods verified by this case. This advance in higher-order measures enables SID to explain why Holism posits that some systems cannot be decomposed without loss of characteristics under existing measures, and offers a potential quantitative framework for higher-order relationships across a broad spectrum of disciplines.

Mingzhe Yang

Papers

Service Composition in Cyber-Physical-Social Systems

Survey on QoE Assessment Approach for Network Service

An overview of compute first networking

Survey on tracking and registration technology for mobile augmented reality

System Information Decomposition