Convolutional Neural Networks （CNN） are used for image forensics because of their high recognizable property， easy understanding， and strong learnability. However， their inherent disadvantages of the receptive field increasing slowly and neglecting long-range dependencies， and high computational cost cause the unsatisfactory accuracy and lightweight deployment of deep learning algorithms. To solve the above problems， a lightweight network-based image copy-paste tamper detection algorithm namely LKA-EfficientNet （Large Kernel Attention EfficientNet） was proposed. The characteristics of long-range dependencies and global receptive field were contained in LKA-EfficientNet， and the number of EfficientNetV2 parameters was optimized. As a result， the localization speed and detection accuracy of image tamper were improved. Firstly， the image was inputted into and processed in the backbone network based on Large Kernel Attention （LKA） to obtain the candidate feature maps. Then， the feature maps of different scales were used to construct the feature pyramid for feature matching. Finally， the candidate feature maps after feature matching were fused to locate the tampered area of the image. In addition， the triple cross entropy loss function was used by LKA-EfficientNet to further improve the accuracy of the algorithm in image tamper localization. Experimental results show that LKA-EfficientNet can not only reduce the floating-point operations by 29.54% but also increase the F1 by 4.88% compared to the same type algorithm — Dense-InceptionNet. The above verifies that LKA-EfficientNet can reduce computational cost and maintain high detection performance at the same time.

On the premise of ensuring the properties of nodes in the network， the purpose of attribute network representation learning is to learn the low-dimensional dense vector representation of nodes by combining structure and attribute information. In the existing attribute network representation learning methods， the learning of attribute information in the network is ignored， and the interaction of attribute information with the network topology is insufficient， so that the network structure and attribute information cannot be fused efficiently. In response to the above problems， a Dual auto-Encoder Network Representation Learning （DENRL） algorithm was proposed. Firstly， the high-order neighborhood information of nodes was captured through a multi-hop attention mechanism. Secondly， a low-pass Laplacian filter was designed to remove the high-frequency signals and iteratively obtain the attribute information of important neighbor nodes. Finally， an adaptive fusion module was constructed to increase the acquisition of important information through the consistency and difference constraints of the two kinds of information， and the encoder was trained by supervising the joint reconstruction loss function of the two auto-encoders. Experimental results on Cora， Citeseer， Pubmed and Wiki datasets show that DENRL algorithm has the highest clustering accuracy and the lowest algorithm running time on three citation network datasets compared with DeepWalk， ANRL （Attributed Network Representation Learning） and other algorithms， achieves these two indicators of 0.775 and 0.460 2 s respectively on Cora datasets， and has the highest link prediction precision on Cora and Citeseer datasets， reaching 0.961 and 0.970 respectively. It can be seen that the fusion and interactive learning of attribute and structure information can obtain stronger node representation capability.

The current supervised face forgery video detection methods need a large amount of labeled data. In order to solve the practical problems of fast iteration and many kinds of video forgery methods， the unsupervised idea in temporal anomaly detection was introduced into face forgery video detection， the face forgery video detection task was transformed into unsupervised video anomaly detection task， and an unsupervised face forgery video detection method based on reconstruction error was proposed. Firstly， the facial landmark sequence of continuous frames in the video to be detected was extracted. Secondly， the facial landmark sequence in the video to be detected was reconstructed based on multi-granularity information such as deviation features， local features and temporal features. Thirdly， the reconstruction error between the original sequence and the reconstructed sequence was calculated. Finally， the score was calculated according to the peak frequency of the reconstruction error to detect the forgery video automatically. Experimental results show that compared with detection methods such as LRNet （Landmark Recurrent Network） and Xception-c23， the proposed method has the AUC （Area Under Curve） of the detection performance increased by up to 27.6%， and the AUC of the transplantation performance increased by 30.4%.

For the problems of limited bandwidth resources， the existence of external disturbance and parameter uncertainty， a non-fragile dissipative control scheme for event-triggered networked systems was proposed. Firstly， based on the Networked Control System （NCS） model， a non-periodic sampling event-triggered scheme was proposed， and a delay closed-loop system model was established. Then， a novel bilateral Lyapunov functional was constructed by using the structure characteristics of sawtooth wave. Finally， the sufficient conditions to ensure the stability of the system were derived by using methods such as Jensen inequality， free weight matrix and convex combination， and the gain of the feedback controller was calculated. The results of numerical simulation show that the proposed bilateral functional is less conservative than the unilateral functional， the event-triggered mechanism can save bandwidth compared with the common sampling mechanism， and the proposed controller is feasible.

For that the Teaching-Learning-Based Optimization （TLBO） algorithm has some problems， such as prematurity and poor solution accuracy， in solving high-dimensional optimization problems， an Improved TLBO algorithm with Adaptive Competitive learning （ITLBOAC） was proposed. Firstly， a weighted parameter with nonlinear change was introduced into the “teaching” operator to determine the ability of the current individual to maintain its own state and adjust the attitude of the current individual towards learning from teachers. As a result， the current individual learnt more from the teacher in the early stage to improve its own state quickly， and kept the state of itself more in the later stage to slow down the influence of the teacher on it. Then， based on ecological cooperation and competition mechanisms， a “learning” operator based on adaptive competition between nearest neighbor individuals was introduced. To make the current individual chose its near neighbors and the individuals eventually shifted from cooperative evolution to competitive learning. Test results on 12 Benchmark test functions show that compared with four improved TLBO algorithms， the proposed algorithm is better in terms of accuracy of solutions， stability and convergence speed， and is much better than TLBO algorithm at the same time， which verify that the proposed algorithm is suitable for solving high-dimensional continuous optimization problems. Test results with compression spring and three-bar truss design problems selected to test show that the optimal values obtained by ITLBOAC decreased by 3.03% and 0.34% respectively， compared with those obtained by TLBO algorithm. It can be seen that ITLBOAC is a trustworthy algorithm in solving constrained engineering optimization problems.

With the development of high-throughput sequencing technology， massive genome sequence data provide a data basis to understand the structure of genome. As an essential part of genomics research， splice site identification plays a vital role in gene discovery and determination of gene structure， and is of great importance for understanding the expression of gene traits. To address the problem that existing models cannot extract high-dimensional features of DNA （DeoxyriboNucleic Acid） sequences sufficiently， a splice site prediction model consisted of BERT （Bidirectional Encoder Representations from Transformers） and parallel Convolutional Neural Network （CNN） was constructed， namely BERT-splice. Firstly， the DNA language model was trained by BERT pre-training method to extract the contextual dynamic association features of DNA sequences and map DNA sequence features with a high-dimensional matrix. Then， the DNA language model was used to map the human reference genome sequence hg19 data into a high-dimensional matrix， and the result was adopted as input of parallel CNN classifier for retraining. Finally， a splice site prediction model was constructed on the basis of the above. Experimental results show that the prediction accuracy of BERT-splice model is 96.55% on the donor set of DNA splice sites and 95.80% on the acceptor set， which improved by 1.55% and 1.72% respectively， compared to that of the BERT and Recurrent Convolutional Neural Network （RCNN） constructed prediction model BERT-RCNN. Meanwhile， the average False Positive Rate （FPR） of donor/acceptor splice sites tested on five complete human gene sequences is 4.74%. The above verifies that the effectiveness of BERT-splice model for gene splice site prediction.

Aiming at the complicated and time-consuming problems of brushed Direct-Current （DC） motor Proportion Integral Differential （PID） parameter tuning， a PID parameter tuning method based on improved Genetic Algorithm （GA） was proposed. Firstly， a fitness enhanced elimination through selection rule was proposed， which improved the selection process of traditional GA. Then， a gene infection crossover method was proposed to ensure the increase of the average fitness value in the evolution process. Finally， the unnecessary copy operation in traditional GA was deleted to improve the running speed of the algorithm. Modeling and simulation analysis were carried out through the motor transfer function. Experimental results show that， compared with conventional tuning methods， the proposed improved GA can significantly improve the PID parameter tuning effect. At the same time， compared with the traditional GA， the improved GA reduces the evolutionary generation number required to achieve the same evolutionary effect by 79%， and increases the running speed of the algorithm by 4.1%. The proposed improved GA improves GA from the two key operation steps of selection and crossover， and is applied to PID parameter tuning to make the rise time less， the stability time shorter， and the overshoot smaller.

To solve the problems of large energy consumption and scarcity of spectrum resources in the traditional Internet of Things （IoT）， a Multiple Input Multiple Output-Ambient Backscatter Communication （MIMO-AmBC） system model which is constructed by an ambient backscatter， a Cooperative Receiver （CRx） and ambient Radio Frequency （RF） source was proposed. First， the system model was analyzed by using the Parasitic Symbiotic Radio （PSR） scheme to derive the Signal-to-Noise Ratio （SNR）. Secondly， the approximate expressions for the ergodic rates of the primary link and the backscatter link were derived， and the maximum expression for the ergodic rate of the backscatter link was obtained. Finally， the proposed system model was compared with the traditional cellular network and Commensal Symbiotic Radio （CSR） scheme. The experimental results verify the correctness of the theoretical derivation and give some meaningful conclusions：1） the backscatter link rate increases with the logarithm of the number of receiving antennas and has nothing to do with the number of transmitting antennas； 2） when the SNR is 10 dB， the sum rate of the PSR scheme is higher than those of the traditional scheme and the CSR scheme by 36.8% and 29.9% respectively. Although the primary link rate of the PSR scheme is 5.5% lower than that of the CSR scheme， the ergodic rate of the backscatter link is 7.7 times higher than that of the CSR scheme， which provides theoretical reference for choosing the AmBC symbiosis scheme for practical applications.

In order to improve the credibility of the damage degree evaluation to the aviation network due to cascading failures caused by emergency， considering the redundancy ability of airport nodes for the load， which means if the overload occurs in a certain spatial range， the node will not fail immediately but has a certain overload handling ability， an aviation network cascading failure model was proposed based on overload condition and failure probability. Firstly， the overload coefficient， weight coefficient， distribution coefficient， and capacity coefficient were introduced into the traditional "load-capacity" Motter-Lai cascading failure model. Then， the redundant capacity characteristics of network nodes were described by overload condition and failure probability， and different load redistribution strategies were applied to the failed and overloaded nodes to make the model more consistent with the aviation network reality. Theoretical analysis and simulation results show that increasing the overload coefficient within a certain range helps to reduce the impact of cascading failures， but the improvement effect is not obvious after increasing to a certain degree； with the optimal intervals for parameters of the model. the aviation network can maintain better robustness while spending smaller construction cost， and the optimized allocation of aviation network resources can improve the network’s resistance to cascading failures.

With the increasing scale and complexity of computer softwares， code defect in software has become a serious threat to public safety. Aiming at the problems of poor expansibility of static analysis tools， as well as coarse detection granularity and unsatisfactory detection effect of existing methods， a static code defect detection method based on program slicing and semantic feature fusion was proposed. Firstly， key points in source code were analyzed through data flow and control flow， and the program slicing method based on Interprocedural Finite Distributive Subset （IFDS） was adopted to obtain the code snippet composed of multiple lines of statements related to code defects. Then， semantically related vector representation of code snippet was obtained by word embedding， so that the appropriate length of code snippet was selected with the accuracy guaranteed. Finally， Text Convolutional Neural Network （TextCNN） and Bi-directional Gate Recurrent Unit （BiGRU） were used to extract local key features and context sequence features of the code snippet respectively， and the proposed method was used to detect slice-level code defects. Experimental results show that the proposed method can detect different types of code defects effectively， and is significantly better than static analysis tool Flawfinder. Under the premise of fine granularity， IFDS slicing method can further improve F₁ score and accuracy，reach 89.64% and 92.08% respectively. Compared with the existing methods based on program slicing， when key points are the Application Programming Interface （API） or the variables， the proposed method has the F₁ score reached 89.69% and 89.74% respectively， and the accuracy reached 92.15% and 91.98% respectively， and all of them are higher. It can be seen that without significantly increasing time complexity， the proposed method has a better comprehensive detection performance.

The traditional image encryption with scrambling-diffusion structure is usually divided into two independent steps of scrambling and diffusion， which are easy to be cracked separately， and the encryption process has weak nonlinearity， resulting in poor security of the algorithm. Therefore， a scrambling diffusion synchronous image encryption algorithm with strong nonlinearity was proposed. Firstly， a new sine-cos chaotic mapping was constructed to broaden the range of control parameters and improve the randomness of sequence distribution. Then， the exclusive-OR sum of plaintext pixels and chaotic sequence was used as the initial chaotic value to generate chaotic sequence， and this chaotic sequence was used to construct the network structures of different pixels of different plaintexts. At the same time， the diffusion value was used to dynamically update the network value to make the network dynamic. Finally， the single pixel serial scrambling-diffusion was used to generate cross-effect between scrambling and diffusion，and the overall synchronization of scrambling and diffusion， so as to effectively resist separation attacks. In addition， the pixel operations were transferred according to the network structure， which made the serial path nonlinear and unpredictable， thereby ensuring the nonlinearity and security of the algorithm. And the adjacent node pixels sum was used to perform dynamic diffusion in order to improve the correlation of the plaintext. Experimental results show that the proposed algorithm has high encryption security， strong plaintext sensitivity， and is particularly effective in anti-statistical attack， anti-differential attack and anti-plaintext attack.

Aiming at the problem of low clinical practicability and accuracy in the clinical diagnosis of myocardial infarction， an auxiliary diagnosis method of myocardial infarction based on 12-lead ElectroCardioGram （ECG） signal was proposed. Firstly， denoising and data enhancement were performed on the 12-lead ECG signals. Secondly， aiming at the ECG signals of each lead， the statistical features including standard deviation， kurtosis coefficient and skewness coefficient were extracted respectively to reflect the morphological characteristics of ECG signals， meanwhile the entropy features including Shannon entropy， sample entropy， fuzzy entropy， approximate entropy and permutation entropy were extracted to characterize the time and frequency spectrum complexity， the new mode generation probability， the regularity and the unpredictability of the ECG signal time series as well as detect the small changes of ECG signals. Thirdly， the statistical features and entropy features of ECG signals were fused. Finally， based on the random forest algorithm， the performance of algorithm was analyzed and verified in both intra-patient and inter-patient modes， and the cross-validation technology was used to avoid over-fitting. Experimental results show that， the accuracy and F1 value of the proposed method in the intra-patient modes are 99.98% and 99.99% respectively， the accuracy and F1 value of the proposed method in the inter-patient mode are 94.56% and 97.05% respectively； and compared with the detection method based on single-lead ECG， the detection of myocardial infarction with 12-lead ECG is more logical for doctors’ clinical diagnosis.

Aiming at the sharp increasing of data on the cloud caused by the development and popularization of cloud native technology as well as the bottlenecks of the technology in performance and stability， a Haystack-based storage system was proposed. With the optimization in service discovery， automatic fault tolerance and caching mechanism， the system is more suitable for cloud native business and meets the growing and high-frequent file storage and read/write requirements of the data acquisition， storage and analysis industries. The object storage model used by the system satisfies the massive file storage with high-frequency reads and writes. A simple and unified application interface is provided for business using the storage system， a file caching strategy is applied to improve the resource utilization， and the rich automated tool chain of Kubernetes is adopted to make this storage system easier to deploy， easier to expand， and more stable than other storage systems. Experimental results indicate that the proposed storage system has a certain performance and stability improvement compared with the current mainstream object storage and file systems in the situation of large-scale fragmented data storage with more reads than writes.

Due to problems of over-compression by a non-adaptive mapping function, and changes of perceived contrasts for luminance shift during mapping, a hierarchical tone-mapping algorithm for detail-preserving was proposed. In this algorithm, the luminance-response curve adapting to each local luminance in High Dynamic Range (HDR) images, as a mapping function, was used to map luminances of the base layer. Then, compensation coefficients of the detail layer, for stretching or compressing details, were computed according to values of luminance shift based on Stevens' effect. The experimental results show that the proposed algorithm has good performance on preserving perceived details.

Heavy land clutter with antenna scan is the main interference for Foreign Object Debris (FOD) detection. However, traditional Constant False Alarm Rate (CFAR) in space-domain is ineffective to detect targets on runways. To solve this problem, a cell-average clutter-map CFAR was proposed. First of all, an echo model based on the characteristics of FOD surveillance radar system was built. Then, the range-bearing two-dimensional CFAR detection could be realized by using clutter-map cells dividing, cell averaging and recursive filtering. Further analysis of the main factors that affected the detection performance of this method was studied in the end. The simulation results show that, the proposed algorithm can effectively detect the weak target and obtain high detection probability with a low signal-to-clutter ratio.

The classical regression systems modeling methods suppose that the training data are sufficient, but partial information missing may weaken the generalization abilities of the regression systems constructed based on this dataset. In order to solve this problem, a regression system with the transfer learning abilities, i.e. Transfer learning Support Vector Regression (T-SVR for brevity) was proposed based on support vector regression. T-SVR could use the current data information sufficiently, and learn from the existing useful historical knowledge effectively, so that remedy the information lack in the current scene. Reinforced current model was obtained through controlling the similarity between current model and history model in the object function and current model can benefit from history scene when information is missing or insufficient. The experiments on simulation data and real data show that T-SVR has better adaptability than the traditional regression modeling method in the scene with information missing.

The conflicts between the real-time, efficient intelligent analysis and the inefficient, laborious trouble shooting, which are faced by most of video surveillance systems, can be resolved by Intelligent Video Quality Detection System (IVQDS). As a part of IVQDS, video jitter detection algorithm was focused in this paper. In the proposed method, sparse optical flow features were fused together with interest point matching algorithm; correctly matched point-set which was reliably detected according to the forward-backward error criterion, was used to estimate the global motion parameters, from which motion entropy was computed to measure the motion homogeneity of the video fragment. The experimental results tested on realistic surveillance video records have shown that the proposed algorithm can work under real-time environment against the effects from big movements with high detection performance.

The image quality assessment is to give a reasonable assessment for the quality of image processing algorithm, and No-Reference (NR) quality evaluation method is applied in a lot of situations of being unable to get the original reference image. The result of structure analysis of the infrared image shows that the uncertainty of the image is fuzzy, but not random. Therefore, the concept of fuzzy entropy was introduced into the quality assessment of infrared image. A method of no-reference quality assessment for blurred infrared image was proposed, comparisons and analysis on performance of the algorithm were given from the following aspects: efficiency, consistency and accuracy. The simulation results show that this method has the characteristics of low computation complexity, fast operation speed and consistence of subjective and objective evaluations. And the general performance is better than the assessment based on Mean Squared Error (MSE) and Peak Singal-to-Noise Ratio (PSNR).

In order to solve low accuracy, large time consumption and limited application range in traditional network traffic classification, a semisupervised network traffic classification method of Support Vector Machine (SVM) was proposed. During the training of SVM, it determined the support vectors from the initial and new sample set by using incremental learning technology, avoided unnecessary repetition training, and improved the situation of original classifiers’ low accuracy and timeconsuming as a result of new samples that appeared. This paper also proposed an improved Tri-training method to train multiple classifiers, and a large number of unlabeled samples and a small amount of labeled samples were used to modify the classifiers, which reduced auxiliary classifier’s noise data and overcame the strict limitation of sample types and traditional Coverification for classification methods. The experimental results show that the proposed algorithm has excellent accuracy and speed in traffic classification.

To study the effect of meteorological conditions on Unmanned Aerial Vehicle (UAV), an algorithm of multi-level fuzzy comprehensive evaluation method based on threat value was proposed. This algorithm improved a two-level weight value determination and the comprehensive evaluation model, which can get the comprehensive threat index after being calculated. The simulation results show that this algorithm can assess the degree of weather threat accurately and have faster operation speed, smaller error and lower complexity. The efficiency and validity have also been improved.

Due to the mutability of unstrained or handwritten digits, most algorithms in previous study either forfeited easy implementation for high accuracy, or vice versa. This paper proposed a new feature descriptor named Distance Distribution Histogram (DDH) and adapted Shape Accumulate Histogram (SAH) feature descriptor based on shape context which was not only easy to implement, but also was robust to noise and distortion. To make hybrid features more comprehensive, some other adapted topological features were combined. The new congregated features were complementary as they were formed from different original feature sets extracted by different means. What's more, they were not complicate. Meanwhile, three Support Vector Machine (SVM) with different feature vector were used as classifier and their results were integrated to get the final classification. The average accurate rate of several experiments based on self-established data sets, MNIST and USPS is as high as 99.21%, which demonstrates that the proposed algorithm is robust and effective.

Direct towards anonymity and high dynamic of online user in P2P TV, a P2P TV crawler, called TVCrawler was developed and deployed which enabled to launch an active measurement on several popular large scale P2P TV systems. The authors conducted a comparative research on time evolution and geographic distribution of online users in these different P2P TV systems. First, while intuitively researching the time evolution of online users in P2P TV channel, the method of MultiScale Entropy (MSE) analysis was introduced to investigate the complexity in time series of the number of online users. Second, the authors made a study on the regular pattern of online users' geographic distribution, and made a Google map-based visual representation about online users. Then, by analyzing the relationship between geographic distribution of online Chinese users and provincial economic development level of China, it is discovered that significant linear decreasing correlation exists between the two of them.

Abstract：To the blemish of PERT in the project plan management, this thesis introduces a kind of optimization arithmetic which is called PSO and offers an advanced technique to PERT on PSO. This technique using the method of treating with the time of the tasks in project and the theory of PSO made a advantage to the traditional PERT. The experimental results show that the technique show a more advantage, more exact ration controlling standard, can get better controlling and regulating ability to the whole project process compared with the traditional PERT.

The H.264 communication over network may cause a whole frame loss. To solve the problem, an error-concealment algorithm based on residual distribution was proposed for whole frame packet loss in H.264. Firstly, the residual information of reference frame was analyzed. Then, according to the result, the motion vector copy algorithm was used in the region where the image was smooth or the image object's motion was rigid. For the other regions, the motion vector of each pixel was re-estimated, and then optical flow algorithm was introduced. Experimental results show that the algorithm outperforms the traditional one on both visual quality and Peak Signal-to-Noise Ratio (PSNR).

The macro handover has caused high packet loss and long handover latency in Hierarchical Mobile IPv6 (HMIPv6) protocol. To solve these problems, this paper proposed a protocol named Tunnel-based Fast Macro-Handover (TBFMH), which introduced the mechanism of tunnel, acquired care-of addresses on the grounds of handover information, conducted duplication address detection in advance and completed local binding update while building the tunnels. The simulation results show that TBFMH can decrease the handover latency by 50% at least and reduce the packet loss rate compared to HMIPv6, which effectively improves the performance in the macro handover.

The Standard Particle Filter (SPF) is a typical method of solving the tracking problem of non-linear/non-Gaussian model system. However, updating process strictly depends on parameters selection, and it cannot handle the changes in curve topology. In regard to this, a new particle filter target tracking algorithm based on geometric active contours was proposed, which made a good deal with the changes of curve topology using level set theory. The algorithm improved the resampling techniques and increased the diversity of particles. The simulation results indicate that the proposed method can effectively improve the state estimation precision with more flexibility.

To improve bottom-layered information resource addressing efficiency for Internet of Things (IoT), with researching trust evaluation criteria on bottom-layered addressing services for IoT in cloud, and improving trust-driven algorithms, an information resource addressing model based on trust-driven cloud for IoT was presented. First the key addressing features were analyzed, then the addressing model was constructed by designing and using specific constraint conditions, trust steepness function, cloud trust evaluation criteria and trust constraint coefficients. Finally, the model was validated by an IoT system designing instance. The experimental results show the proposed model has satisfactory bottom-layered resource addressing efficiency in comparison with traditional models or algorithms.

Classic decision tree algorithm is unfit to cope with uncertain data pervaded at both the construction and classification phase. In order to overcome these limitations， D-S decision tree classification algorithm was proposed. This algorithm extended the decision tree technique to an uncertain environment. To avoid the combinatorial explosion that would result from tree construction phase, uncertainty measure operator and aggregation combination operator were introduced. This D-S decision tree is a new classification method applied to uncertain data and shows good performance and can efficiently avoid combinatorial explosion.

Concerning the inefficient problem of traditional signature-based virus filtering algorithm in practice, a novel virus filtering algorithm based on Matrix Bloom Filter (MBF) was proposed. Based on the analysis of the space efficiency, time efficiency and the potential effects of false positives, the mathematical model of the algorithm was studied and the design scheme of virus filters in high-speech engine was given. Finally, the simulation experimental results demonstrate the effectiveness and practicability of the proposed algorithm.

Ad Hoc network often supplies reliable authentication service for nodes by distributed certificate authority authentication model due to its dynamic topology and other characteristics; however, the problem of secure auditing is not solved by existing schemes. Therefore, a distributed secret share distribution scheme was put forward based on threshold warrant certificates, which carried out strict auditing towards the nodes that applied for secret shares, and could effectively withstand several malicious nodes working together to recover the secret key of system, and make sure that only the credible and high-quality-service nodes were able to gain the secret shares. In the end, the security and success probability of the scheme were analyzed in theory, and its effectiveness was confirmed by simulations.