By replacing traditional hardware-based middleboxes with software-based Virtual Network Functions (VNFs) running on general-purpose servers, network function virtualization represents a promising technique to reduce the cost of service creation and increase the agility of network operations. Typically, Service Function Chains (SFCs) are adopted to orchestrate dynamical network services and facilitate management of network applications. Recently, SFC parallelism that implements parallel processing of VNFs has been investigated to further improve SFC service quality. However, the unreasonable service graph of parallel processing in existing parallelized SFCs (PSFCs) might cause excessive resource consumption; incoordination between PSFC deployment and scheduling also increases the queuing delay of VNFs and degrades PSFC performance. In this article, an adaptive parallel processing optimization mechanism (APPM) is proposed to self-adaptively adjust the service graph of PSFCs and intelligently solve the joint problem of PSFC deployment and scheduling. Specifically, APPM uses a parallelism optimization algorithm (POA) based on the bin packing problem with soft bin capacity to optimize the structure of the PSFC service graph. Afterward, APPM employs a joint optimization algorithm based on reinforcement learning (JORL) to jointly deploy and schedule the PSFCs optimized by POA via the online perception of environment status. Simulation results showed that POA reduces the SFC parallelism degree and resource consumption by about 35%; JORL lowers SFC delay by reducing the queuing delay and has better overall performance than the state of the art algorithms even with limited resources.

Efficient anomaly detection methods are urgently needed to prevent attacks in the application layer of the Industrial Internet of Things (IIoT). The existing intrusion detection systems have certain limitations in detecting abnormal packets exploited by the application-layer attacks. In this article, a content-agnostic-payload-based anomaly detector named the CapBad is proposed to detect malicious packets in the application layer of the IIoT system. Specifically, a phase-aware hidden semi-Markov model (pHSMM) is used to model the industrial control protocol packets and automatically learn the packets’ payload characteristics. The packet types are then inferred based on the packet likelihoods obtained by the pHSMM. In addition, the probabilistic suffix tree is employed to analyze the packets’ contextual similarity to the historical packets. The abnormal packets are finally detected by comparing their contextual similarity with that of the historical normal packets. The proposed algorithm is verified by simulations, and the results show that the CapBad has an excellent performance in detecting abnormal packets in the application layer.

By caching the most popular content into mobile devices, users can retrieve content directly from nearby devices through Device to Device (D2D) communications, which can significantly reduce backhaul traffic and improve network performance. Most existing D2D cache placement strategies are proactive approaches, which cannot deal with the problem of timely cache updating. In this paper, we propose a network coding-based socially-aware D2D caching strategy, which takes geographical proximity and the social relationships of users into consideration. First, a physical D2D network with high communication reliability is built according to the geographical proximity, composed of devices with high probability of communicating to each other through stable D2D communications. According to the social relationship between users within the physical D2D network, we partition the devices into communities and rank the devices within same community by their influence degree. Within a community, each caching decision is made independently according to the user-content contribution degree. To calculate the contribution degree, the impact between devices on requesting same content is modeled using an Indian Buffet Process. Devices cache coded blocks instead of the whole content to improve caching efficiency. Simulation results show that the proposed strategy achieves higher cache hit and sum rates compared to other schemes.

Centrality analysis is one of the most important and commonly used tools in social networks. For social networks where edges are just present or absent and have no more information attached, many centrality measures have been presented, such as degree, closeness, betweenness, eigenvector and Laplacian centrality. There has been a growing need to design centrality measures for fuzzy enterprise technology innovation cooperation networks (FETICNs), because FETICNs where edges are attached with fuzzy technical cooperation relation would contain rich information. In this paper, we propose some new centrality measures called fuzzy logarithm attenuation closeness centrality and fuzzy logarithm attenuation closeness centralization which are applicable to the FETICNs. It unveils more structural information about fuzzy technical cooperation relation, attenuation factor, and connectivity of the FETICNs. Furthermore, we investigate the validness of a new centrality measure by illustrating this method to an experimental study and obtain reliable results, which provide strong evidences to the new measure’s utility.

With device-to-device (D2D) communication, user equipment can share data with each other without the involvement of network infrastructures. In order to maintain the Quality of Service (QoS) and Quality of Experience (QoE) for user applications in D2D communications, most existing schemes use proactive content caching that needs to predict content popularity before making caching decisions which may result in privacy leakage, since the information of users is collected to train a deep learning-based model to predict content popularity. Therefore, it is crucial to guarantee secure data collection in machine learning-based framework. In this paper, we propose a privacy-preserving D2D caching scheme with a passive content caching strategy based on node importance, which can deliver more efficient caching and prevent the potential leakage of user privacy. The scheme is based on softwaredefined networking (SDN), in which the controller is responsible for calculating node importance of devices according to the information of requests and encounters collected by SDN switches. Base station will decide which device can establish reliable and secure communication with content requester based on historical information. The simulation results show that the proposed strategy can outperform other D2D caching strategies in terms of cache hit rate and data rate.