In the high squint TOPS mode SAR imaging of the maneuvering platform,by using the BP imaging algorithm in the rectangular coordinate system of the ground plane,the wide swath SAR image without distortion in the ground plane can be obtained in a short time.However,how to quickly complete the motion error compensation and side lobe suppression of the BP image is still difficult in practical application.This paper proposes an improved spectral compression method,which can quickly realize the follow-up operations such as autofocus of the BP image of the ground plane in the high squint TOPS mode of the mobile platform.First,by considering that the traditional BP spectral compression method is only applicable to the spotlight imaging mode,combined with the virtual rotation center theory of high-squint TOPS SAR and the wavenumber spectrum analysis,an improved exact spectral compression function is derived,which can give rise to the unambiguous ground plane TOPS mode BP image spectrum through full-aperture compression,on the basis of which the phase gradient autofocus(PGA) can be used to quickly complete the full aperture motion error estimation and compensation.In addition,based on the unambiguous aligned BP image spectrum obtained by the improved spectral compression method proposed in this paper,the image sidelobe suppression can be realized by uniformly windowing in the azimuth frequency domain.Finally,the effectiveness of the proposed algorithm is verified by simulation data processing.

Quantum error-correcting codes are the key way to address the issue caused by the inevitable noise along with the quantum computing process.Spatially coupled quantum LDPC codes,as their classical counterparts,can achieve a good balance between the error-correcting capacity and the decoding delay in principle.By considering the problems of high complexity and long decoding delay caused by the standard belief propagation algorithm(BPA) for decoding the spatially coupled quantum LDPC codes(SC-QLDPCs),a quantum version of the sliding decoding scheme,named the double window sliding decoding algorithm is proposed in this paper.The proposed algorithm is inspired by the idea of classical sliding window decoding strategies and by exploiting the non-zero diagonal bands on the principal and sub-diagonals structure of the corresponding two parity-check matrices(PCMs) of the concerned SC-QLDPC.The phase and bit flipping error syndromes of the received codeword are obtained by sliding the two windows along the principal and sub-diagonals of the two classical PCMs simultaneously,which enables a good trade-off between complexity and decoding delay to be obtained by using the proposed strategy,with numerical results given to verify the performance of the proposed double window sliding decoding scheme.Simulation results show that the proposed algorithm can not only offer a low latency decoding output but also provide a decoding performance approaching that of the standard BPA when enlarging the window size,thus improving the application scenarios of the SC-QLDPC significantly.

In emerging industries such as unmanned aerial vehicles and drones,the signal coverage requirements are high,not only in the city,but in the inaccessible mountains,deserts,and forests also wireless signal coverage is needed to truly complete remote control.These areas need to consider the impact of terrain changes on electromagnetic transmission.The Uniform Geometrical Theory of Diffraction method in Computational Electromagnetic is an effective method to analyze electromagnetic problems in electrically large environments and this paper uses the method of computational electromagnetics to study the propagation of electromagnetic waves in mountainous environments.A new method of constructing an irregular terrain model is presented.The available terrain data can be generated by the cubic surface algorithm,and the irregular terrain is spliced by multiple cubic surfaces.The accuracy of the model data is verified by the mean root mean square error.Based on the topographic data,a parallel 3D geometric optical algorithm is completed,and the distribution of the regional electromagnetic field is simulated.The actual mountain terrain environment is selected for field measurement,and the comparison trend between the measurement results and the simulation results is consistent,which verifies the effectiveness of the method in the analysis of electromagnetic wave propagation in the irregular terrain.Considering the scale of environmental electromagnetic computation,a parallel strategy is established,and the parallel efficiency of 100 cores test can be kept to be above 80%.

With the development of wireless communication technology,the research on communication technology in high-speed scenario is becoming more and more extensive,one aspect of which is that obtaining accurate channel state information is of great significance to improving the performance of a wireless communication system.In order to solve the problem that the existing channel prediction algorithms for orthogonal Frequency Division multiplexing(OFDM) systems do not consider the influence of noise and the low prediction accuracy in high-speed scenarios,a time-varying channel prediction algorithm based on attention denoising and complex convolution LSTM is proposed.First,a channel attention channel denoising network is proposed to denoise the channel state information,which reduces the influence of noise on the channel state information.Second,a channel prediction model based on the complex convolutional layer and long short term memory(LSTM) is constructed.The channel state information at the historical moment after denoising is extracted,and then it is input into the channel prediction model to predict the channel state information at the future moment.The improved LSTM prediction model enhances the ability to extract channel timing features and improves the accuracy of channel prediction.Finally,the Adam optimizer is used to predict the channel state information at the future time.Simulation results show that the proposed time-varying channel prediction algorithm based on the attention denoising and complex convolutional LSTM network method has a higher prediction accuracy for the channel state information than the comparison algorithm.At the same time,the proposed method can be applied to the time-varying channel prediction in high-speed moving scenarios.

Radar systems are subject to an ever-changing and complex environment that creates a non-uniform and time-varying clutter.The unsuppressed residual clutter can produce a significant number of false alarms,leading to a degraded target tracking performance,spurious trajectories creation,or saturation data processing systems,which in turn decreases the detection ability of the radar system.Conventional residual clutter suppression algorithms typically require feature extraction and classifier construction.These steps can result in poor generalization capability,difficulty in feature combination,and high requirements for the classifier.To address these issues,inspired by self-attention mechanisms and domain knowledge,this paper proposes a data- and knowledge-driven attention autocorrelation mechanism,which can effectively extract deep features of the radar echo to distinguish between targets and clutter,on the basis of which a residual clutter suppression method is constructed using the attention autocorrelation mechanism,which makes full use of the radar echo feature,thereby improving the residual clutter suppression capability.Simulation and measurement results demonstrate that this method has advantages of a significant performance and generalization capability for residual clutter suppression.Additionally,its parallel computing structure enhances the operational efficiency of the algorithm.

Network Function Virtualization(NFV) has become the key technology of next generation communication.Virtual Network Function Service Chain(VNF-SC) mapping is the key issue of the NFV.To reduce the energy consumption of the communication network server and improve the quality of service,a Function Chain(SFC) deployment algorithm based on an improved Double Deep Q Network(DDQN) is proposed to reduce the energy consumption of network servers and improve the network quality of service.Due to the dynamic change of the network state,the service function chain deployment problem is modeled as a Markov Decision Process(MDP).Based on the network state and action rewards,the DDQN is trained online to obtain the optimal deployment strategy for the service function chain.To solve the problem that traditional deep reinforcement learning draws experience samples uniformly from the experience replay pool leading to low learning efficiency of the neural network,a prioritized experience replay method based on importance sampling is designed to draw experience samples so as to avoid high correlation between training samples to improve the learning efficiency of the neural network.Experimental results show that the proposed SFC deployment algorithm based on the improved DDQN can increase the reward value,and that compared with the traditional DDQN algorithm,it can reduce the energy consumption and blocking rate by 19.89%~36.99% and 9.52%~16.37%,respectively.

The emergence of new technologies such as Software-Defined Network,Network Function Virtualization,and Intent-Driven Network have driven the development of networks towards service-oriented,customized,and intelligent directions.However,the large and complex network infrastructure has led to network management failures and frequent security attacks,making it crucial to improve network service resilience and achieve continuous network service assurance.The Intent-Driven Network can automate the entire process of generating and deploying network resilience strategies from user intent.This provides networks with more flexible means to effectively address a wide array of challenges,greatly improving the network management efficiency and enhancing network service resilience,on the basis of which the paper proposes an intent-driven network service resilience control loop architecture and its implementation architecture.By introducing the Belief-Desire-Intention(BDI) reasoning logic into the resilience reasoning mechanism,the network is endowed with preventive,defensive,restorative,and adaptive functionalities,enabling networks to respond promptly in the early stages of network attacks,adjusting resilience strategies flexibly based on specific contexts,countering sudden network assaults,and sustaining network service assurance.Finally,the proposed intent-driven network service resilience mechanism is validated for its effectiveness in ensuring network service resilience using Distributed Denial of Service(DDoS) attacks as a use case.

With the rapid enlargement of the air transportation scale,the aviation ad hoc network(AANET) communication based on the civil aviation aircraft has possessed the capacities of communication network coverage.To find an effective means of important data transmission of aircraft nodes in highly dynamic and uncertain complex scenarios and backup them safely has become more important for improving the reliability and management abilities of the air-space-ground integrated network.However,the characteristics of the AANET,such as high dynamic change of network topology,large network span,and unstable network links,have brought severe challenges to the design of AANET protocols,especially the routing protocols.In order to facilitate the future research on the design of AANET routing protocols,this paper comprehensively analyzes the relevant requirements of AANET routing protocol design and investigates the existing routing protocols.First,according to characteristics of the AANET,this paper analyzes the factors,challenges,and design principles that need to be considered in the design of the routing protocols.Then,according to the design characteristics of existing routing protocols,this paper classifies and analyzes the existing routing protocols of the AANET.Finally,the future research focus of the routing protocols for the AANET is analyzed,so as to provide reference for promoting the research on the next generation of the air-space-ground integrated network in China.

After several years of rapid development,the collaborative filtering algorithms based on graph convolutional neural networks have achieved the most advanced performance in many recommender system scenarios.However,most of these algorithms only use simple random negative sampling method when collecting negative samples,and do not make full use of graph structure information.To solve this problem,a graph convolution neural network for recommendation using graph negative sampling(GCN-GNS) is proposed.The algorithm first constructs a user-item bipartite graph and uses a graph convolution neural network to obtain the node embedding vector.Next,the depth-first random walk method is used to obtain the sequence of the wandering item nodes that includes both the neighboring item nodes and the distant item nodes.Then the attention layer is designed to learn the weights of different nodes in the walk sequence adaptively and a dynamically updated virtual negative sample is formed according to the weights.Finally,the virtual negative sample is used to train the model more efficiently.Experimental results show that the GCN-GNS performs better than other algorithms for comparison on three real public datasets in most cases,which indicates that the proposed novel graph negative sampling method can help the GCN-GNS model to make better use of the graph structure information,and ultimately improves the effect of item recommendation.

Spectral relaxation is widely used in traditional subspace clustering and spectral clustering.First,the eigenvector of the Laplacian matrix is calculated.The eigenvector contains negative numbers,and the result of the 2-way clustering can be obtained directly according to the positive and negative of the elements.For multi-way clustering problems,2-way graph partition is applied recursively or the k-means is used in eigenvector space.The assignment of the cluster label is indirect.The instability of clustering results will increase by this post-processing clustering method.For the limitation of spectral relaxation,a subspace clustering algorithm optimized by non-negative Lagrangian relaxation is proposed,which integrates self-representation learning and rank constraints in the objective function.The similarity matrix and membership matrix are solved by non-negative Lagrangian relaxation and the nonnegativity of the membership matrix is maintained.In this way,the membership matrix becomes the cluster posterior probability.When the algorithm converges,the clustering results can be obtained directly by assigning the data object to the cluster with the largest posterior probability.Compared with the existing subspace clustering and spectral clustering methods,the proposed algorithm designs a new optimization rule,which can realize the direct allocation of cluster labels without additional clustering steps.Finally,the convergence of the proposed algorithm is analyzed theoretically.Generous experiments on five benchmark clustering datasets show that the clustering performance of the proposed method is better than that of the recent subspace clustering methods.

In order to improve the ability of multimedia to analyze the security on Web and effectively filter the objectionable content,a violent video scene detection algorithm based on three-dimensional attention is proposed.Taking the 3D DenseNet as the backbone network,the algorithm first uses the P3D to extract low-level spatial-temporal feature information.Second,the SimAM attention module is introduced to calculate channel-spatial attention so as to enhance the feature of the key area in the video frame.Then,a transition layer with temporal attention is designed to highlight the feature of key frames in the video.In this way,the channel-spatial-temporal attention is formed to better detect violent scenes.In the experiments on violence detection,the accuracy reaches 98.75% and 100% on Hockey and Movies,which are small data sets with a single content,and 89.25% on RWF-2000,which is a large data set with a diverse content.Results show that the proposed algorithm can effectively improve the performance of violence detection with 3D attention.In the violent content localization detection experiment on data set VSD2014,the better performance further proves the effectiveness and generalization ability of the algorithm.

To improve the accuracy of the semantic segmentation models and avoid the labor and time costs of pixel-wise image annotation for large-scale semantic segmentation datasets,this paper studies the pre-training methods of self-supervised contrastive representation learning,and designs the Global-Local Cross Contrastive Learning(GLCCL) method based on the characteristics of the semantic segmentation task.This method feeds global images and a series of image patches after local chunking into the network to extract global and local visual representations respectively,and guides the network training by constructing loss function that includes global contrast,local contrast,and global-local cross contrast,enabling the network to learn both global and local visual representations as well as cross-regional semantic correlations.When using this method to pre-train BiSeNet and transfer to the semantic segmentation task,compared with the existing self-supervised contrastive representational learning and supervised pre-training methods,the performance improvement of 0.24% and 0.9% mean intersection over union(MIoU) is achieved.Experimental results show that this method can improve the segmentation results by pre-training the semantic segmentation model with unlabeled data,which has a certain practical value.

Image is an important carrier of information dissemination in the era of the mobile Internet,making malicious image tampering one of the potential cybersecurity threats.Different from the image tampering on the object scale in the natural scene,image tampering in the real world exists in forged qualification certificates,forged documentation,forged screenshots,etc.The tampered images in the real world usually involve elaborate manual tampering interventions,so their tampering features are different from those in the natural scene and are more diverse,making the localization of tampered areas in the real world more challenging.Rich dependency information is important in considering the complex and diverse tampering features in the real world.Therefore,in this paper,the convolutional neural network is used for adaptive feature extraction and the reversely connected fully self-attention module is adopted for multi-stage feature attention.Finally,the tamper area is located by merging the multi-stage attentional results.The proposed method outperforms the comparison methods in the real world image tampering localization task with the F1 metric 8.98% higher than that of the mainstream method MVSS-Net and the AUC metric 3.58% higher.Besides,the proposed method also achieves the performance of mainstream methods in the natural scene image tampering localization task,and the evidence that the natural scene tampering features are inconsistent with the real world tampering features is provided.Experimental results in two scenes show that the proposed method can effectively locate the tampered area of the tampered images,and that it is more effective in complicated real world.

The smoke segmentation is challenging because the smoke is irregular and translucent and the boundary is fuzzy.A dual-branch real-time smoke segmentation algorithm based on global and local information is proposed to solve this problem.In this algorithm,a lightweight Transformer branch and a convolutional neural networks branch are designed to extract the global and local features of smoke respectively,which can fully learn the long-distance pixel dependence of smoke and retain the details of smoke.It can distinguish smoke and background accurately and improve the accuracy of smoke segmentation.It can satisfy the real-time requirement of the actual smoke detection tasks.The multilayer perceptron decoder makes full use of multi-scale smoke features and further models the global context information of smoke.It can enhance the perception of multi-scale smoke,and thus improve the accuracy of smoke segmentation.The simple structure can reduce the computation of the decoder.The algorithm reaches 92.88% mean intersection over union on the self-built smoke segmentation dataset with 2.96M parameters and a speed of 56.94 frames per second.The comprehensive performance of the proposed algorithm is better than that of other smoke detection algorithms on public dataset.Experimental results show that the algorithm has a high accuracy and fast inference speed.The algorithm can meet the accuracy and real-time requirements of actual smoke detection tasks.

In recent years,various robust algorithms and defense schemes have been presented to prevent the harm caused by abnormal traffic to the federal encrypted traffic classification model.The existing defense methods,which improve the robustness of the global model by removing the traffic of abnormal models,are coarse-grained.Nevertheless,the coarse-grained methods can lead to issues of excessive defense and normal traffic loss.To solve the above problems,we propose a fine-grained defense method to avoid abnormal traffic according to the collaborative federated encrypted traffic classification framework.The proposed method narrows the range of the abnormal traffic by dividing the local data set of abnormal nodes,achieving fine-grained localization of abnormal nodes.According to the localization results of abnormal traffic,the method realizes the fine-grained defense by eliminating abnormal traffic during model aggregation,which avoids the excessive defense and normal traffic loss.Experimental results show that the proposed method can significantly improve the efficiency of model detection without affecting accuracy.Compared with the existing coarse-grained methods,the accuracy of the fine-grained defense method can reach 91.4%,and the detection efficiency is improved by 32.3%.

The rational use of patient medical and health data information has promoted the development of medical research institutions.Aiming at the current difficulties in sharing medical data between patients and medical research institutions,data privacy is easy to leak,and the use of medical data is uncontrollable,a medical data privacy protection scheme supporting controlled sharing is proposed.Firstly,the blockchain and proxy server are combined to design a medical data controlled sharing model that the blockchain miner nodes are distributed to construct proxy re-encryption keys,and the proxy server is used to store and convert medical data ciphertext,and proxy re-encryption technology is used to bring about the secure sharing of medical data while protecting the privacy of patients.Secondly,a dynamic adjustment mechanism of user permissions is designed that the patient and the blockchain authorization management nodes update the access permissions of medical data through the authorization list to realize the controllable sharing of medical data by patients.Finally,the security analysis shows that the proposed scheme can bring about the dynamic sharing of medical data while protecting the privacy of medical data,and can also resist collusion attacks.Performance analysis shows that this scheme has advantages in communication overhead and computing overhead,and is suitable for controlled data sharing between patients or hospitals and research institutions.

Smart contracts are programs deployed on the blockchain that enable distributed transactions.However,due to the financial attributes and immutable characteristics of smart contracts,they become targets of hacker attacks.Therefore,to ensure the security of contracts,it is necessary to repair vulnerable contracts.However,existing contract vulnerability repair schemes have problems such as low repair success rate and inability to handle complex contracts.To this end,a contract vulnerability repair scheme supporting inline data processing is proposed in this paper.The proposed scheme first studies and formalizes the dynamic loading mechanism of the Ethereum virtual machine,and constructs an inline data location algorithm based on memory copy instructions to parse and decompile the smart contract bytecode structure;then the smart contract bytecode is rewritten based on the trampoline mechanism,and the inline data address offset caused by rewriting is corrected,and finally the smart contract vulnerability repair is implemented.A prototype tool named SCRepair is implemented based on the proposed scheme,which is deployed on the local test network Ganache for performance testing,and compared with existing vulnerability repair tools EVMPatch and Smartshield.Experimental results show that the SCRepair improves the bytecode rewrite success rate by 26.9% when compared with the EVMPatch.Besides,the SCRepair has a better rewrite execution stability,and is less affected by the compiler version;the SCRepair can handle complex contracts better when compared with the Smartshield.

With the development of cloud computing,more enterprises and individuals tend to outsource their data to cloud storage providers to relieve the local storage pressure,and the cloud storage pressure is becoming an increasingly prominent issue.To improve the storage efficiency and reduce the communication cost,data deduplication technology has been widely used.There are identical data deduplication based on the hash table and similar data deduplication based on the bloom filter,but both of them rarely consider the impact of data popularity.In fact,the data outsourced to the cloud storage can be divided into popular and unpopular data according to their popularity.Popular data refer to the data which are frequently accessed,and there are numerous duplicate copies and similar data in the cloud,so high-accuracy deduplication is required.Unpopular data,which are rarely accessed,have fewer duplicate copies and similar data in the cloud,and low-accuracy deduplication can meet the demand.In order to address this problem,a novel bloom filter variant named PDBF(popularity dynamic bloom filter) is proposed,which incorporates data popularity into the bloom filter.Moreover,a PDBF-based deduplication scheme is constructed to perform different degrees of deduplication depending on how popular a datum is.Experiments demonstrate that the scheme makes an excellent tradeoff among the computational time,the memory consumption,and the deduplication efficiency.

The spread control of disinformation is a hot area of global cyberspace security governance.At present,the research on the spread control of disinformation in online social networks has not considered the actual problem of the cost incurred by the control of key nodes set.This paper proposes a disinformation spreading control model based on key nodes bi-objective optimization.First,according to the spread influence of social user nodes in the 1-hop and 2-hop areas,as well as the degree centrality of nodes,k-shell and other complex network characteristics,the bi-objective including the control effect and control cost is expressed mathematically.Second,a bit flipping mutation algorithm incorporating adaptive nonlinear strategy is designed to improve the performance of the NSGA-Ⅱ algorithm in discrete search space.The improved NSGA-Ⅱ algorithm is used to select a key nodes set of disinformation spreading,which maximizes the effect of disinformation spreading control and minimizes the control cost.Finally,the experiment is carried out on a real online social network platform,with the influence of model parameters on the control cost and control effect analyzed and discussed.Experimental results show that this model has specific and obvious advantages over the existing methods in the combination index R_TCTE of control cost and control effect.This model is applicable to the lowest cost disinformation spreading control in large-scale complex social networks.

In order to further study the application of the neural network in cryptanalysis,the neural network differential divider of several typical lightweight block cipher algorithms is constructed and improved by using a deep residual network and traditional differential cryptanalysis techniques.The main results are as follows.First,the neural distinguishers of 4 to 7 rounds of PRESENT,3 rounds of KLEIN,7 to 9 rounds of LBlock and 7 to 10 rounds of Simeck32/64 are constructed and analyzed respectively based on the block cipher structure.Second,based on the characteristics of SPN structure block ciphers,PRESENT and KLEIN's neural distinguishers are improved,which can improve the accuracy of about 5.12% at most.In the study of LBlock’s neural distinguisher,it is verified that this improved method is not suitable for Feistel structure block ciphers.Third,based on the characteristics of the simeck 32/64 cryptography algorithm,the neural distinguisher is improved,with the accuracy improved by 2.3%.Meanwhile,the improved method of Simeck 32/64 is combined with the polyhedral difference analysis,and the accuracy of the existing 8-round and 9-round Simeck 32/64 poly neural network difference partition is increased by 1% and 3.2%.Finally,the three types of neural distinguishers obtained in the experiment are applied to the last round key recovery attack of 11-round simeck 32/64,with the best experimental result being a 99.4% success rate with 26.6 data complexity in 1 000 attacks.