Network Intrusion Traffic Detection Based on Feature Extraction

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

Spatio-Temporal Variability of Rainfall and Temperature and Their Effects on Pasture Variability over East Africa: Implication on the Cattle Grazing Areas

Understanding the spatiotemporal variability of climatic parameters and their effects on pasture variability is vital for pasture management interventions over East Africa. The present study aims to assess the spatial-temporal variability of rainfall, temperature and Normalized Difference Vegetation Index (NDVI) (which is being used to assess pasture quality and productivity) over the region, between the period of 1982 and 2019. This study used annual mean values for rainfall, temperature and NDVI which were calculated for the period mentioned above. NDVI was derived from National Oceanic and Atmospheric Administration (NOAA) Global Area Cover (GAC) (NOAA-07-GAC) data. The rainfall data was acquired from the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) while temperature is ERA5 reanalysis data sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study utilized the empirical orthogonal function (EOF) to identify patterns and dominant relationships between the climate variables. The correlation was calculated between rainfall, temperature and NDVI to assess the relationship among them. A non-parametric Mann-Kendall trends test was used to determine whether annual precipitation, temperature and NDVI had statistically increasing or decreasing trend. Results revealed a positive correlation between rainfall and NDVI while a negative correlation between NDVI and temperature. Positive correlation between rainfall and NDVI indicates that pasture health (quality and productivity), will improve accordingly. A negative correlation between temperature and NDVI indicates that pasture health will decrease with increase in temperature while improving with decreasing temperature. Outcome from this study suggests that changes in climatic variables influence the distribution of pasture in East Africa’s cattle grazing areas. The study hence recommends prioritisation of climatic (weather) information during pasture management over East Africa.

Computing Power Network:The Architecture of Convergence of Computing and Networking towards 6G Requirement

In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.

Performance analysis of quantum access network using code division multiple access model

A quantum access network has been implemented by frequency division multiple access and time division multiple access, while code division multiple access is limited for its difficulty to realize the orthogonality of the code. Recently,the chaotic phase shifters were proposed to guarantee the orthogonality by different chaotic signals and spread the spectral content of the quantum states. In this letter, we propose to implement the code division multiple access quantum network by using chaotic phase shifters and synchronization. Due to the orthogonality of the different chaotic phase shifter, every pair of users can faithfully transmit quantum information through a common channel and have little crosstalk between different users. Meanwhile, the broadband spectra of chaotic signals efficiently help the quantum states to defend against channel loss and noise.

A NOVEL FRAMEWORK OF ONLINE NETWORK CODING FOR MULTICAST SWITCHES WITH CONSTRAINED BUFFERS

Network coding is able to address output conflicts when fanout splitting is allowed for multicast switching.Hence,it successfully achieves a larger rate region than non-coding approaches in crossbar switches.However,network coding requires large coding buffers and a high computational cost on encoding and decoding.In this paper,we propose a novel Online Network Coding framework called Online NC for multicast switches,which is adaptive to constrained buffers.Moreover,it enjoys a much lower decoding complexity by a Vandermonde matrix based approach,as compared to conven-tional randomized network coding Our approach realizes online coding with one coding algo-rithm that synchronizes buffering and coding.Therefore,we significantly reduce requirements on buffer space,while also sustaining high throughputs.We confirm the superior advantages of our contributions using empirical studies.

Analysis of Different Call Admission Control Strategies and Its Impact on the Performance of LTE-Advanced Networks

The call admission control (CAC) optimizes the use of allocated channels against offered traffic maintaining the required quality of service (QoS). Provisioning QoS to user at cell-edge is a challenge where there is limitation in cell resources due to inter-cell interference (ICI). Soft Frequency Reuse is ICI mitigation scheme that controls the distribution of resources between users. In this paper, the Impact of four CAC schemes (Cutoff Priority scheme (CP), Uniform Fractional Guard Channel (UFGC), Limited Fractional Guard Channel (LFGC), New Call Bounding (NCB) scheme) at cell-edge have investigated using queuing analysis in a comparative manner. The comparison is based on two criteria. The first criterion guarantees a particular level of service to already admitted users while trying to optimize the revenue obtained. The second criterion determines the minimum of number of radio resources that provides hard constraints in both of blocking and dropping probabilities. The four schemes are compared at different scenarios of new and handover call arrival rates.

Performance Assessment for LTE-Advanced Networks with Uniform Fractional Guard Channel over Soft Frequency Reuse Scheme

Dropping probability of handoff calls and blocking probability of new calls are two important Quality of Service (QoS) measures for LTE-Advanced networks. Applying QoS for Cell edge users in soft frequency reuse scheme in LTE system is a challenge as they already suffer from limited resources. Assigning some resources for handover calls may enhance dropping probability but this is in price of degradation in the blocking probability for new calls in cell-edge. Uniform Fractional Guard Channel (UFGC) is a call admission policy that provides QoS without reserving resources for handover calls. In this paper, the performance of Soft Frequency Reuse (SFR) in presence of Uniform Fractional Guard Channel (UFGC) will be investigated using queuing analysis. The mathematical model and performance metrics will be deduced in this assessment. The impact of UFGC will be evaluated in edge and core part separately. Then the optimal value for the parameter of UFGC will be obtained to minimize the blocking probability of new calls with the constraint on the upper bound on the dropping probability of handoff calls.

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related features.RS has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features.This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images(ODLTCP-HRRSI)to resolve these issues.The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities.To attain this,the presented ODLTCP-HRRSI model performs two major processes.At the initial stage,the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic flow.Next,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)algorithm.The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy

The advent of single-cell RNA sequencing(scRNA-seq)has provided insight into the tumour immune microenvironment(TIME).This review focuses on the application of scRNA-seq in investigation of the TIME.Over time,scRNA-seq methods have evolved,and components of the TIME have been deciphered with high resolution.In this review,we first introduced the principle of scRNA-seq and compared different sequencing approaches.Novel cell types in the TIME,a continuous transitional state,and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer.Thus,we concluded novel cell clusters of cancerassociated fibroblasts(CAFs),T cells,tumour-associated macrophages(TAMs)and dendritic cells(DCs)discovered after the application of scRNA-seq in TIME.We also proposed the development of TAMs and exhausted T cells,as well as the possible targets to interrupt the process.In addition,the therapeutic interventions based on cellular interactions in TIME were also summarized.For decades,quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer.Summarizing the current findings,we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy,which can subsequently be implemented in the clinic.Finally,we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.

Generation of hyperentangled photon pairs based on lithium niobate waveguide

Generation of hyperentangled photon pairs is investigated based on the lithium niobate straight waveguide.We propose to use the nonlinear optical process of spontaneous parametric down-conversion(SPDC)and a well-designed lithium niobate waveguide structure to generate a hyperentangled(in the polarization dimension and the energy-time dimension)two-photon state.By performing numerical simulations of the waveguide structure and calculating the possible polarization states,joint spectral amplitudes(JSA),and joint temporal amplitudes(JTA)of the generated photon pair,we show that the generated photon pair is indeed hyperentangled in both the polarization dimension and the energy-time dimension.

Compact generation scheme of path–frequency hyperentangled photons using 2D periodical nonlinear photonic crystal

Hyperentanglement is a promising resource for achieving high capacity quantum communication.Here,we propose a compact scheme for the generation of path-frequency hyperentangled photon pairs via spontaneous parametric down-conversion(SPDC)processes,where six different paths and two different frequencies are covered.A two-dimensional periodicalχ^((2))nonlinear photonic crystal(NPC)is designed to satisfy type-Ⅰquasi-phase-matching conditions in the plane perpendicular to the incident pump beam,and a perfect phase match is achieved along the pump beam's direction to ensure high conversion efficiency,with theoretically estimated photon flux up to 2.068×10^(5) pairs·s^(-1)·mm^(-2).We theoretically calculate the joint-spectral amplitude(JSA)of the generated photon pair and perform Schmidt decomposition on it,where the resulting entropy S of entanglement and effective Schmidt rank K reach 3.2789 and 6.4675,respectively.Our hyperentangled photon source scheme could provide new avenues for high-dimensional quantum communication and high-speed quantum information processing.

Digital twin modeling and controlling of optical power evolution enabling autonomous-driving optical networks:a Bayesian approach

Optical networks are evolving toward ultrawide bandwidth and autonomous operation.In this scenario,it is crucial to accurately model and control optical power evolutions(OPEs)through optical amplifiers(OAs),as they directly affect the signal-to-noise ratio and fiber nonlinearities.However,a fundamental contradiction arises between the complex physical phenomena in optical transmission and the required precision in network control.Traditional theoretical methods underperform due to ideal assumptions,while data-driven approaches entail exorbitant costs associated with acquiring massive amounts of data to achieve the desired level of accuracy.In this work,we propose a Bayesian inference framework(BIF)to construct the digital twin of OAs and control OPE in a data-efficient manner.Only the informative data are collected to balance the exploration and exploitation of the data space,thus enabling efficient autonomous-driving optical networks(ADONs).Simulations and experiments demonstrate that the BIF can reduce the data size for modeling erbium-doped fiber amplifiers by 80%and Raman amplifiers by 60%.Within 30 iterations,the optimal controlling performance can be achieved to realize target signal/gain profiles in links with different types of OAs.The results show that the BIF paves the way to accurately model and control OPE for future ADONs.

Multiscale strain alleviation of Ni-rich cathode guided by in situ environmental transmission electron microscopy during the solid-state synthesis

Ni-rich layered oxides are one of the most promising cathode materials for Li-ion batteries due to their high energy density.However,the chemomechanical breakdown and capacity degradation associated with the anisotropic lattice evolution during lithiation/delithiation hinders its practical application.Herein,by utilizing the in situ environmental transmission electron microscopy(ETEM),we provide a real time nanoscale characterization of high temperature solid-state synthesis of LiNi_(0.8)CO_(0.1)Mn_(0.1)O_(2)(NCM811) cathode,and unprecedentedly reveal the strain/stress formation and morphological evolution mechanism of primary/second ary particles,as well as their influence on electrochemical performance.We show that stress inhomogeneity during solid-state synthesis will lead to both primary/secondary particle pulverization and new grain boundary initiation,which are detrimental to cathode cycling stability and rate performance.Aiming to alleviate this multiscale strain during solid-state synthesis,we introduced a calcination scheme that effectively relieves the stress during the synthesis,thus mitigating the primary/secondary particle crack and the detrimental grain boundaries formation,which in turn improves the cathode structural integrity and Li-ion transport kinetics for long-life and high-rate electrochemical performance.This work remarkably advances the fundamental understanding on mechanochemical properties of transition metal oxide cathode with solid-state synthesis and provides a unified guide for optimization the Ni-rich oxide cathode.

Joint Allocation of Computing and Connectivity Resources in Survivable Inter-Datacenter Elastic Optical Networks

Inter-datacenter elastic optical networks(EON)need to provide the service for the requests of cloud computing that require not only connectivity and computing resources but also network survivability.In this paper,to realize joint allocation of computing and connectivity resources in survivable inter-datacenter EONs,a survivable routing,modulation level,spectrum,and computing resource allocation algorithm(SRMLSCRA)algorithm and three datacenter selection strategies,i.e.Computing Resource First(CRF),Shortest Path First(SPF)and Random Destination(RD),are proposed for different scenarios.Unicast and manycast are applied to the communication of computing requests,and the routing strategies are calculated respectively.Simulation results show that SRMLCRA-CRF can serve the largest amount of protected computing tasks,and the requested calculation blocking probability is reduced by 29.2%,28.3%and 30.5%compared with SRMLSCRA-SPF,SRMLSCRA-RD and the benchmark EPS-RMSA algorithms respectively.Therefore,it is more applicable to the networks with huge calculations.Besides,SRMLSCRA-SPF consumes the least spectrum,thereby exhibiting its suitability for scenarios where the amount of calculation is small and communication resources are scarce.The results demonstrate that the proposed methods realize the joint allocation of computing and connectivity resources,and could provide efficient protection for services under single-link failure and occupy less spectrum.

Recent advances of heterogeneously integrated Ⅲ–Ⅴ laser on Si

Due to the indirect bandgap nature,the widely used silicon CMOS is very inefficient at light emitting.The integration of silicon lasers is deemed as the‘Mount Everest’for the full take-up of Si photonics.The major challenge has been the materials dissimilarity caused impaired device performance.We present a brief overview of the recent advances of integratedⅢ-Ⅴlaser on Si.We will then focus on the heterogeneous direct/adhesive bonding enabling methods and associated light coupling structures.A selected review of recent representative novel heterogeneously integrated Si lasers for emerging applications like spectroscopy,sensing,metrology and microwave photonics will be presented,including DFB laser array,ultra-dense comb lasers and nanolasers.Finally,the challenges and opportunities of heterogeneous integration approach are discussed.

Multi-channel 28-GHz millimeter-wave signal generation on a silicon photonic chip with automated polarization control

We propose and experimentally demonstrate an integrated silicon photonic scheme to generate multi-channel millimeter-wave(MMW) signals for 5 G multi-user applications. The fabricated silicon photonic chip has a footprint of 1.1 × 2.1 mm^2 and integrates 7 independent channels each having on-chip polarization control and heterodyne mixing functions. 7 channels of4-Gb/s QPSK baseband signals are delivered via a 2-km multi-core fiber(MCF) and coupled into the chip with a local oscillator(LO) light. The polarization state of each signal light is automatically adjusted and aligned with that of the LO light, and then 7 channels of 28-GHz MMW carrying 4-Gb/s QPSK signals are generated by optical heterodyne beating. Automated polarizationcontrol function of each channel is also demonstrated with ～7-ms tuning time and ～27-dB extinction ratio.

Diffusion-weighted imaging of pancreatic cancer

Magnetic resonance imaging(MRI) is a reliable and accurate imaging method for the evaluation of patients with pancreatic ductal adenocarcinoma(PDAC). Diffusion-weighted imaging(DWI) is a relatively recent technological improvement that expanded MRI capabilities, having brought functional aspects into conventional morphologic MRI evaluation. DWI can depict the random diffusion of water molecules within tissues(the so-called Brownian motions). Modifications of water diffusion induced by different factors acting on the extracellular and intracellular spaces, as increased cell density, edema, fibrosis, or altered functionality of cell membranes, can be detected using this MR sequence. The intravoxel incoherent motion(IVIM) model is an advanced DWI technique that consent a separate quantitative evaluation of all the microscopic random motions that contribute to DWI, which are essentially represented by molecular diffusion and blood microcirculation(perfusion). Technological improvements have made possible the routine use of DWI during abdominal MRI study. Several authors have reported that the addition of DWI sequence can be of value for the evaluation of patients with PDAC, especially improving the staging; nevertheless, it is still unclear whether and how DWI could be helpful for identification, characterization, prognostic stratification and follow-up during treatment. The aim of this paper is to review up-to-date literature data regarding the applications of DWI and IVIM to PDACs.