查看更多>>摘要：With the evolvement of the Internet of things(IoT),mobile edge computing(MEC)has emerged as a promising computing paradigm to support IoT data analysis and processing.In MEC for IoT,the differentiated re-quirements on quality of service(QoS)have been growing rapidly,making QoS a multi-dimensional concept includ-ing several attributes,such as performance,dependability,energy efficiency,and economic factors.To guarantee the QoS of IoT applications,theories and techniques of multi-dimensional QoS evaluation and optimization have become important theoretical foundations and supporting technologies for the research and application of MEC for IoT,which have attracted significant attention from both academia and industry.This paper aims to survey the existing studies on multi-dimensional QoS evaluation and optimization of MEC for IoT,and provide insights and guidance for future research in this field.This paper summarizes the multi-dimensional and multi-attribute QoS metrics in IoT scenarios,and then several QoS evaluation methods are presented.For QoS optimization,the main research problems in this field are summarized,and optimization models as well as their corresponding solutions are elaborated.We take notice of the booming of edge intelligence in artificial intelligence-empowered IoT scenarios,and illustrate the new research topics and the state-of-the-art approaches related to QoS evaluation and optimization.We discuss the challenges and future research directions.
查看更多>>摘要：Low earth orbit(LEO)satellite edge computing can overcome communication difficulties in harsh environments,which lack the support of terrestrial communication infrastructure.It is an indispensable option for achieving worldwide wireless communication coverage in the future.To improve the quality-of-service(QoS)for Internet-of-things(IoT)devices,we combine LEO satellite edge computing and ground communication systems to provide network services for IoT devices in harsh environments.We study the QoS-aware computation offloading(QCO)problem for IoT devices in LEO satellite edge computing.Then we investigate the computation offloading strategy for IoT devices that can minimize the total QoS cost of all devices while satisfying multiple constraints,such as the computing resource constraint,delay constraint,and energy consumption constraint.We formulate the QoS-aware computation offloading problem as a game model named QCO game based on the non-cooperative competi-tion game among IoT devices.We analyze the finite improvement property of the QCO game and prove that there is a Nash equilibrium for the QCO game.We propose a distributed QoS-aware computation offloading(DQCO)algo-rithm for the QCO game.Experimental results show that the DQCO algorithm can effectively reduce the total QoS cost of IoT devices.
查看更多>>摘要：The rapid development of Internet of things(IoT)and edge computing technologies has brought forth numerous possibilities for the intelligent and digital future.The frequent communication and interaction between devices inevitably generate a large amount of sensitive information.Deploying a blockchain network to store sensitive data is crucial for ensuring privacy and security.The openness and synchronicity of blockchain networks give rise to challenges such as transaction privacy and storage capacity issues,significantly impeding their development in the context of edge computing and IoT.This paper proposes a reliable fog computing service solution based on a blockchain fog architecture.This paper stores data files in the inter planetary file system(IPFS)and encrypts the file hash values used for retrieving data files with stream cipher encryption.It employs a steganographic transmission technique leveraging AlphaZero's Gomoku algorithm to discretely transmit the stream cipher key across the block-chain network without a carrier,thus achieving dual encryption.This approach aims to mitigate the storage burden on the blockchain network while ensuring the security of transaction data.Experimental results demonstrate that the model enhances the transmission capacity of confidential information from kilobytes(KB)to megabytes(MB)and exhibits high levels of covert and security features.
查看更多>>摘要：Mobile edge computing(MEC)provides edge services to users in a distributed and on-demand way.Due to the heterogeneity of edge applications,deploying latency and resource-intensive applications on resource-constrained devices is a key challenge for service providers.This is especially true when underlying edge infrastruc-tures are fault and error-prone.In this paper,we propose a fault tolerance approach named DFGP,for enforcing mobile service fault-tolerance in MEC.It synthesizes a generative optimization network(GON)model for predicting resource failure and a deep deterministic policy gradient(DDPG)model for yielding preemptive migration decisions.We show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service,in terms of fault detection accuracy,migration efficiency,task migration time,task scheduling time,and energy consumption than other existing methods.
查看更多>>摘要：As a pivotal enabler of intelligent transportation system(ITS),Internet of vehicles(IoV)has aroused extensive attention from academia and industry.The exponential growth of computation-intensive,latency-sensitive,and privacy-aware vehicular applications in IoV result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs)closer to vehicles for efficient execution.In ITS environment,however,due to dynamic and stochastic computation offloading requests,it is challenging to efficiently orchestrate offloading decisions for application requirements.How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging.In this paper,we propose an intelligent computation offloading with privacy protection scheme,named COPP.In particular,an Advanced Encryption Standard-based encryption method is uti-lized to implement privacy protection.Furthermore,an online offloading scheme is proposed to find optimal offloading policies.Finally,experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.
查看更多>>摘要：Complex networks are becoming more complex because of the use of many components with diverse technologies.In fact,manual configuration that makes each component interoperable has breed latent danger to sys-tem security.There is still no comprehensive review of these studies and prospects for further research.According to the complexity of component configuration and difficulty of security assurance in typical complex networks,this pa-per systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks,specifically analyzes,and compares the current key technologies such as configuration semantic awareness,automatic generation of security configuration,dynamic deployment,and verification evaluation.These technologies can effectively improve the security of complex networks intelligent configuration and reduce the com-plexity of operation and maintenance.This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system,which lays a theoretical founda-tion for the formation of the comprehensive effectiveness verification capability of configuration security.The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.
查看更多>>摘要：With the increasing deployment of deep learning-based systems in various scenes,it is becoming important to conduct sufficient testing and evaluation of deep learning models to improve their interpretability and robustness.Recent studies have proposed different criteria and strategies for deep neural network(DNN)testing.However,they rarely conduct effective testing on the robustness of DNN models and lack interpretability.This paper proposes a new priority testing criterion,called DeepLogic,to analyze the robustness of the DNN models from the perspective of model interpretability.We first define the neural units in DNN with the highest average activation probability as"interpretable logic units".We analyze the changes in these units to evaluate the model's robustness by conducting adversarial attacks.After that,the interpretable logic units of the inputs are taken as context attri-butes,and the probability distribution of the softmax layer in the model is taken as internal attributes to establish a comprehensive test prioritization framework.The weight fusion of context and internal factors is carried out,and the test cases are sorted according to this priority.The experimental results on four popular DNN models using eight testing metrics show that our DeepLogic significantly outperforms existing state-of-the-art methods.
查看更多>>摘要：Existing deep learning-based steganography detection methods utilize convolution to automatically capture and learn steganographic features,yielding higher detection efficiency compared to manually designed stegano-graphy detection methods.Detection methods based on convolutional neural network frameworks can extract global features by increasing the network's depth and width.These frameworks are not highly sensitive to global features and can lead to significant resource consumption.This manuscript proposes a lightweight steganography detection method based on multiple residual structures and Transformer(ResFormer).A multi-residuals block based on channel rearrangement is designed in the preprocessing layer.Multiple residuals are used to enrich the residual features and channel shuffle is used to enhance the feature representation capability.A lightweight convolutional and Transformer feature extraction backbone is constructed,which reduces the computational and parameter complexity of the net-work by employing depth-wise separable convolutions.This backbone integrates local and global image features through the fusion of convolutional layers and Transformer,enhancing the network's ability to learn global features and effectively enriching feature diversity.An effective weighted loss function is introduced for learning both local and global features,BiasLoss loss function is used to give full play to the role of feature diversity in classification,and cross-entropy loss function and contrast loss function are organically combined to enhance the expression ability of features.Based on BossBase-1.01,BOWS2 and ALASKA#2,extensive experiments are conducted on the stego images generated by spatial and JPEG domain adaptive steganographic algorithms,employing both classical and state-of-the-art steganalysis techniques.The experimental results demonstrate that compared to the SRM,SRNet,SiaStegNet,CSANet,LWENet,and SiaIRNet methods,the proposed ResFormer method achieves the highest reduction in the parameter,up to 91.82％.It achieves the highest improvement in detection accuracy,up to 5.10％.Compared to the SRNet and EWNet methods,the proposed ResFormer method achieves an improvement in detection accuracy for the J-UNIWARD algorithm by 5.78％and 6.24％,respectively.
查看更多>>摘要：Adversarial examples(AEs)are an additive amalgamation of clean examples and artificially malicious perturbations.Attackers often leverage random noise and multiple random restarts to initialize perturbation starting points,thereby increasing the diversity of AEs.Given the non-convex nature of the loss function,employing random-ness to augment the attack's success rate may lead to considerable computational overhead.To overcome this challenge,we introduce the one-hot mean square error loss to guide the initialization.This loss is combined with the strongest first-order attack,the projected gradient descent,alongside a dynamic attack step size adjustment strategy to form a comprehensive attack process.Through experimental validation,we demonstrate that our method outperforms base-line attacks in constrained attack budget scenarios and regular experimental settings.This establishes it as a reliable measure for assessing the robustness of deep learning models.We explore the broader application of this initialization strategy in enhancing the defense impact of few-shot classification models.We aspire to provide valuable insights for the community in designing attack and defense mechanisms.
查看更多>>摘要：It is an interesting research direction to develop new multi-objective optimization algorithms based on meta-heuristics.Both the convergence accuracy and population diversity of existing methods are not satisfactory.This paper proposes an integrated external archive local disturbance mechanism for multi-objective snake optimizer(IMOSO)to overcome the above shortcomings.There are two improved strategies.The adaptive mating between subpopulations strategy introduces the special mating behavior of snakes with multiple husbands and wives into the original snake optimizer.Some positions are updated according to the dominated relationships between the newly created individuals and the original individuals.The external archive local disturbance mechanism is used to re-search partial non-inferior solutions with poor diversities.The perturbed solutions are non-dominated sorting with the gener-ated solutions by the next iteration to update the next external archive.The main purpose of this mechanism is to make full use of the non-inferior solution information to better guide the population evolution.The comparison re-sults of the IMOSO and 7 state-of-the-art algorithms on WFG benchmark functions show that IMOSO has better convergence and population diversity.
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