Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of servi...
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Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.
Since different kinds of face forgeries leave similar forgery traces in videos,learning the common features from different kinds of forged faces would achieve promising generalization ability of forgery ***,to accurat...
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Since different kinds of face forgeries leave similar forgery traces in videos,learning the common features from different kinds of forged faces would achieve promising generalization ability of forgery ***,to accurately detect known forgeries while ensuring high generalization ability of detecting unknown forgeries,we propose an intra-inter network(IIN)for face forgery detection(FFD)in videos with continual *** proposed IIN mainly consists of three modules,i.e.,intra-module,inter-module,and forged trace masking module(FTMM).Specifically,the intra-module is trained for each kind of face forgeries by supervised learning to extract special features,while the inter-module is trained by self-supervised learning to extract the common *** a result,the common and special features of the different forgeries are decoupled by the two feature learning modules,and then the decoupled common features can be utlized to achieve high generalization ability for ***,the FTMM is deployed for contrastive learning to further improve detection *** experimental results on FaceForensic++dataset demonstrate that the proposed IIN outperforms the state-of-the-arts in ***,the generalization ability of the IIN verified on DFDC and Celeb-DF datasets demonstrates that the proposed IIN significantly improves the generalization ability for FFD.
To address the problem of inaccurate prediction of slab quality in continuous casting, an algorithm based on particle swarm optimisation and differential evolution is proposed. The algorithm combines BP neural network...
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Learning a good similarity measure for large-scale high-dimensional data is a crucial task in machine learning applications, yet it poses a significant challenge. Distributed minibatch Stochastic Gradient Descent (SGD...
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Image deraining is a highly ill-posed *** significant progress has been made due to the use of deep convolutional neural networks,this problem still remains challenging,especially for the details restoration and gener...
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Image deraining is a highly ill-posed *** significant progress has been made due to the use of deep convolutional neural networks,this problem still remains challenging,especially for the details restoration and generalization to real rain *** this paper,we propose a deep residual channel attention network(DeRCAN)for *** channel attention mechanism is able to capture the inherent properties of the feature space and thus facilitates more accurate estimations of structures and details for image *** addition,we further propose an unsupervised learning approach to better solve real rain images based on the proposed *** qualitative and quantitative evaluation results on both synthetic and real-world images demonstrate that the proposed DeRCAN performs favorably against state-of-the-art methods.
The integration of Dynamic Graph Neural Networks(DGNNs)with Smart Manufacturing is crucial as it enables real-time,adaptive analysis of complex data,leading to enhanced predictive accuracy and operational efficiency i...
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The integration of Dynamic Graph Neural Networks(DGNNs)with Smart Manufacturing is crucial as it enables real-time,adaptive analysis of complex data,leading to enhanced predictive accuracy and operational efficiency in industrial *** address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains,and over-smoothing caused by traditional graph neural networks,a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed:Binary Domain Graph Neural Network(BDGNN).The proposed model begins by utilizing a modified Graph Convolutional Network(GCN)without an activation function to extract meaningful graph topology information,ensuring non-redundant *** the temporal domain,Recurrent Neural Network(RNN)and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights,aiming to mitigate the impact of noise within the graph *** the spatial domain,the AdaBoost(Adaptive Boosting)algorithm is applied to replace the traditional approach of stacking layers in a graph neural *** allows for the utilization of multiple independent graph learners,enabling the extraction of higher-order neighborhood information and alleviating the issue of *** efficacy of BDGNN is evaluated through a series of experiments,with performance metrics including Mean Average Precision(MAP)and Mean Reciprocal Rank(MRR)for link prediction tasks,as well as metrics for traffic speed regression tasks across diverse test *** with other models,the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information,but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN.
With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of *** study presents a new approach to the encryption and compression of color *** is predicat...
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With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of *** study presents a new approach to the encryption and compression of color *** is predicated on 2D compressed sensing(CS)and the hyperchaotic ***,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong ***,the processed images are con-currently encrypted and compressed using 2D *** them,chaotic sequences replace traditional random measurement matrices to increase the system’s ***,the processed images are re-encrypted using a combination of permutation and diffusion *** addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct *** with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational ***,it has better *** experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.
Accurate localization of tropical cyclone (TC) centers is crucial for intensity estimation and track prediction. While traditional methodologies primarily rely on visible light and infrared (IR) imagery, the potential...
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Biomass chitosan(CS)was used as a template,graphitic phase carbon nitride(g-C_(3)N_(4))with high nitrogen content and certain catalytic activity was used as a dopant,and nano-transition metal cobalt(Co)was used as a c...
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Biomass chitosan(CS)was used as a template,graphitic phase carbon nitride(g-C_(3)N_(4))with high nitrogen content and certain catalytic activity was used as a dopant,and nano-transition metal cobalt(Co)was used as a catalytic center *** carbon aerogel(C(CS)/g-C_(3)N_(4)/Co)with a three-dimensional network-like structure was prepared by assembling the three materials through experimental operations such as freeze-drying and high-temperature *** was demonstrated by scanning and transmission characterization that the CS in the carbon aerogel could provide more active sites for the cobalt nanoparticles,and the doping of graphite-phase carbon nitride as a template dispersed the cobalt nanoparticles and changed the conductivity of the *** investigate the catalytic effect of carbon aerogel on ammonium perchlorate(AP),it was investigated by differential thermal analyzer and TG thermal *** carbon aerogel was very effective in catalyzing AP,and the 10 wt% content of the catalyst reduced the AP pyrolysis peak from 703.9 to 595.5 *** to further investigate the synergistic effect of the three materials,further carbon aerogels such as C(CS)/Co,g-C_(3)N_(4)/Co were prepared and applied to catalyze AP,and the same ratio reduced the AP pyrolysis peak by 98.1℃ and 97.7℃.This result indicates a synergistic effect of the assembly of the three materials.
With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)...
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With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points(NCPs).Considering the amount of task data and the idle resources of NCPs,a computing resource scheduling model for NCPs is *** the heterogeneous task execution delay threshold as a constraint,the optimization problem is described as the problem of maximizing the utilization of computing resources by *** proposed problem is proven to be NP-hard by using the method of reduction to a 0-1 knapsack problem.A many-to-many matching algorithm based on resource preferences is *** algorithm first establishes the mutual preference lists based on the adaptability of the task requirements and the resources provided by *** enables the filtering out of un-schedulable NCPs in the initial stage of matching,reducing the solution space *** solve the matching problem between ICVs and NCPs,a new manyto-many matching algorithm is proposed to obtain a unique and stable optimal matching *** simulation results demonstrate that the proposed scheme can improve the resource utilization of NCPs by an average of 9.6%compared to the reference scheme,and the total performance can be improved by up to 15.9%.
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