The capability of entrapment is significantly influenced by the degree of simulation in industrial control honeypots. In view of the lack of business logic simulation of existing industrial control honeypots, the high-interaction Programmable Logic Controller (PLC) honeypot design framework and implementation method based on industrial control business simulation were proposed. First, based on the interaction level of industrial control system, a new classification method of Industrial Control System (ICS) honeypots was proposed. Then, according to different simulation dimensions of ICS devices, the entrapment process in honeypot was divided into a process simulation cycle and a service simulation cycle. Finally, in order to realize the real-time response to business logic data, the process data was transferred to the service simulation cycle through a customized data transfer module. Combining typical ICS honeypot software Conpot and the modeling simulation tool Matlab/Simulink, the experiments were carried out with Siemens S7-300 PLC device as the reference, and so as to realize the collaborative work of information service simulation and control process simulation. The experimental results show that compared with Conpot, the proposed PLC honeypot system newly adds 11 private functions of Siemens S7 devices. Especially, the operating read (function code 04 Read) and write (function code 05 Write) in the new functions realize 7 channel monitoring for I area data and 1 channel control for Q area data in PLC. This new honeypot system breaks through the limitations of existing interaction levels and methods and finds new directions for ICS honeypot design.

Non-negative Matrix Factorization (NMF) algorithm can only be used to find low rank approximation of original non-negative data while Concept Factorization (CF) algorithm extends matrix factorization to single non-linear kernel space, improving learning ability and adaptability of matrix factorization. In unsupervised environment, to design or select proper kernel function for specific dataset, a new algorithm called Globalized Multiple Kernel CF (GMKCF) was proposed. Multiple candidate kernel functions were input in the same time and learned in the CF framework based on global linear fusion, obtaining a clustering result with high quality and stability and solving the problem of kernel function selection that the CF faced. The convergence of the proposed algorithm was verified by solving the model with alternate iteration. The experimental results on several real databases show that the proposed algorithm outperforms comparison algorithms in data clustering, such as Kernel K-Means (KKM), Spectral Clustering (SC), Kernel CF (KCF), Co-regularized multi-view spectral clustering (Coreg), and Robust Multiple KKM (RMKKM).

A novel algorithm based on tabu search and combined with simulated annealing was proposed to solve random Constraint Satisfaction Problem (CSP) with growing domain. Firstly, tabu search was used to obtain a set of initial heuristic assignments, which meant a set of candidate solutions were constructed based on a randomly initialized feasible solution through neighborhood, and then the tabu table was used to move the candidate solutions to the direction of minimizing the objective function value. If the obtained optimal assignment was not the solution of the problem, the assignment would be used as the initial heuristic assignment and then simulated annealing was performed to correct the set of assignments until the global optimal solution was obtained. The numerical experiments demonstrate that, the proposed algorithm can effectively find the solution of problem when approaching the theoretical phase transition threshold of problem, and it shows obvious superiority compared with other local search algorithms. The proposed algorithm can be applied to the algorithm design of random CSP.

In view of the high complexity of current probabilistic machine learning methods in solving probability problems, and the fact that traditional Support Vector Data Description (SVDD), as a kernel density estimation method, can only estimate whether the test samples belong to this class, a probability-based SVDD method was proposed. Firstly, the traditional SVDD method was used to obtain the data descriptions of two types of data, and the distance between the test sample and the hypersphere was calculated. Then, a function was constructed to convert the distance into probability, and an SVDD method based on probability was proposed. At the same time, Bagging algorithm was used for the integration to further improve the performance of data description. By referring to classification scenarios, the proposed method was compared with the traditional SVDD method on 13 kinds of benchmark datasets of Gunnar Raetsch. The experimental results show that the proposed method is better than the traditional SVDD method on accuracy and F1-value, and its performance of data description is improved.

Focusing on the issue that the impulse noise in Power Line Communication (PLC) system greatly affects the transmission performance, and most traditional de-noising algorithm can not effectively suppress the impulse noise, a time-frequency combination de-noising algorithm was proposed. Firstly, the impulse noise with large peak in the time domain received signal was detected and zeroed by selecting the appropriate threshold. Secondly, according to the symbols that had been decided in the frequency domain, the smaller impulse noise which had not eliminated in the time domain was reconstructed, and the accuracy of the noise reconstruction was improved by iteration. Finally, the reconstructed impulse noise was subtracted from the frequency domain received signal. Simulation experiments were conducted under the multipath channel of the power line. Compared with traditional time domain and frequency domain de-noising algorithms, the proposed algorithm could achieve the performance improvement of 2dB and 0.5dB respectively when the bit-error rate was 0.01. And as the bit-error rate decreased, the performance gap between them would be even greater. The simulation results show that the proposed time-frequency combination de-noising algorithm can improve the resistance of the PLC system to impulse noise.

Focusing on the problem of robust video object tracking, a robust generative algorithm based on sparse representation was proposed. Firstly, object and background templates were constructed by extracting the image features, and sufficient candidates were acquired by using random sampling method at each frame. Secondly, the sparse coefficient vector was got to structure the similarity map by an innovative optimization formulation named multitask reverse sparse representation formulation, which searched multiple subsets from the whole candidate set to simultaneously reconstruct multiple templates with minimum error. Here a customized Augmented Lagrange Multiplier (ALM) method was derived for solving the L ₁-min problem within several iterations. Finally, the additive pooling was proposed to extract discriminative information in the similarity map for effectively selecting the best candidate which the most similar to the object template and was most different to the background template to be the tracking result, and the tracking was implemented within the Bayesian filtering framework. Moreover, a simple but effective update mechanism was made to update object and background templates so as to handle the object appearance variation caused by illumination change, occlusion, background clutter and motion blur. Compared with the other tracking algorithms, both qualitative and quantitative evaluations on a variety of challenging sequences demonstrate that the tracking accuracy and stability of the proposed algorithm has improved and the proposed algorithm can effectively solve target tracking problem in these scenes of illumination and scale changing, occlusion, complex background, and so on.

The double quantization effect of JPEG (Joint Photographic Experts Group) provides important clues for detecting image tampering. When an original JPEG image undergoes localized tampering and is saved again in JPEG format, the Discrete Consine Transform (DCT) coefficients of untampered regions would undergo double JPEG compressing, while the DCT coefficients of tampered regions would only undergo a single compression. The Alternating Current (AC) coefficient distribution accords with a Laplace probability density distribution described with a suitable parameter. And on this basis, this paper proposed a new double compression probability model of JPEG image to describe the change of DCT coefficients after the double compression, and combined the Bayes criterion to express the eigenvalues of the image blocks which have undergone the single and double JPEG compression. A threshold was set for the eigenvalues. Then the tampered region was automatically detected and extracted by using the threshold to classify the eigenvalues. The experimental results show that the method can detect and locate the tamped area effectively and it outperforms in terms of the detection result compared with the blind detection algorithm of composite images by measuring inconsistencies of JPEG blocking artifact and image forgery detection algorithm based on quantization table especially when the second compression factor is smaller than the first one.

Methods of parallel computation are used in validating topology of polygons stored in simple feature model. This paper designed and implemented a parallel algorithm of validating topology of polygons stored in simple feature model. The algorithm changed the master-slave strategy based on characteristics of topology validation and generated threads in master processor to implement task parallelism. Running time of computing and writing topology errors was hidden in this way. MPI and PThread were used to achieve the combination of processes and threads. The land use data of 5 cities in Jiangsu, China, was used to check the performance of this algorithm. After testing, this parallel algorithm is able to validate topology of massive polygons stored in simple feature model correctly and efficiently. Compared with master-slave strategy, the speedup of this algorithm increases by 20%.

In previous patch-based texture synthesis algorithm, the size of patch requires artificial choice which leads to an uncertainty quality of texture synthesis, so an automatic selection algorithm of patch size for texture synthesis was presented. When the patch was slid on the examplar in scan-line order until getting through all the plane, the histograms of the patch and the examplar were both pretreated by normalization and mean filtering, then the intersection of the two histograms was calculated. Among all the calculation results for different position, the maximum value was selected as the measurement of the color similarity between patches and examplar. Due to approximate monotone increasing relation between color similarity and patchs size, bisection method was adopted to calculate the abscissa for the color similarity threshold point, and the abscissa was used as the patchs size for texture synthesis. The experimental results of various types of textures show that the size of patch automatically selected by this method coincides with the range of best empirical value. Compared with the other automatic selection methods of patchs size, this method not only applies to structured texture synthesis, but also applies to stochastic texture synthesis, and obtains suitable results of texture synthesis.

In allusion to the problem that the computational complexity of Volterra for nonlinear adaptive filtering algorithm increases in power series, a second-order Volterra adaptive filter algorithm based on Set-Membership-Filtering (SMF) under the α-stable distributions noise was proposed. As the object function of SMF involved all signal pairs of input and output, through the threshold judgment of the p square of output errors amplitude the weight vectors of Volterra filter were updated, not only reducing the complexity of filtering algorithm, but also improving the robustness of the adaptive algorithm for input signal correlation. And the update formula of the weight vectors was derived. The simulation results show that the proposed algorithm has lower computational complexity, faster convergence rate, and better robustness against the noise and the input signal correlation.

Topology generation algorithm of weighted directed network based on the triad formation rule was proposed. The directed edges were added to the network using weight preferential attachment rule and triad formation rule; moreover, the weights of network edges were evolved dynamically to realize the asymmetric interaction among nodes. The simulation results show that, the network topology, which is generated by the modeling algorithm for weighted directed network topology based on the triad formation rule, is consistent with topology characteristics showed by topology structures in the real network environment. Meanwhile, it has good controllability of the clustering coefficient.