The second generation of blockchain represented by smart contract has experienced an explosive growth of its platforms and applications in recent years. However, frequent smart contract vulnerability incidents pose a serious risk to blockchain ecosystem security. Since code auditing based on expert experience is inefficient in smart contracts vulnerability mining, the significance of developing universal automated tools to mining smart contracts vulnerability was proposed. Firstly, the security threats faced by smart contracts were investigated and analyzed. Top 10 vulnerabilities, including code reentrancy, access control and integer overflow, as well as corresponding attack modes were summarized. Secondly, mainstream detection methods of smart contract vulnerabilities and related works were discussed. Thirdly, the performance of three existing tools based on symbolic execution were verified through experiments. For a single type of vulnerability, the highest false negative rate was 0.48 and the highest false positive rate was 0.38. The experimental results indicate that existing studies only support incomplete types of vulnerability with many false negatives and positives and depend on manual review. Finally, future research directions were forecasted aiming at these limitations, and a symbolic-execution-based fuzzy test framework was proposed. The framework can alleviate the problems of insufficient code coverage in fuzzy test and path explosion in symbolic execution, thus improving vulnerability mining efficiency for large and medium-sized smart contracts.

To solve the problems in traditional network protocol recognition methods, such as difficulty of manual feature extraction and low recognition accuracy, an application protocol recognition method based on Convolutional Neural Network (CNN) was proposed. Firstly, the raw network data was divided according to Transmission Control Protocol (TCP) connection or User Datagram Protocol (UDP) interaction, and the network flow was extracted. Secondly, the network flow was converted into a two-dimensional matrix through data prepocessing to facilitate the CNN analysis. Then, a CNN model was trained using the training set to extract protocol features automatically. Finally, the trained CNN model was used to recognize the application network protocols. The experimental results show that, the overall recognition accuracy of the proposed method is about 99.70%, which can effectively recognize the application protocols.

Since the protocol classification of network traffic is a prerequisite for protocol analysis and network management, the network protocol recognition techniques were researched and reviewed. Firstly, the target of network protocol recognition was described, and the general process of protocol recognition was analyzed. The practical requirements for protocol recognition were discussed, and the criteria for evaluating protocol recognition methods were given. Then, the research status of network protocol techniques was summarized from two categories:packet-based protocol recognition methods and flow-based protocol recognition methods, and the variety of techniques used for protocol recognition were analyzed and compared. Finally, with the defects of current protocol recognition methods and the practical application requirements considered, the research trend of protocol recognition techniques was forecasted.

To address the underperformance of feature-based image registration under situations with large affine deformation and similar targets, and reduce the time cost, an improved Descriptor-Nets (D-Nets) algorithm based on matching quality purification was proposed. The feature points were detected by Features From Accelerated Segment Test (FAST) algorithm initially, and then they were filtered according to Harris corner response function and meshing. Furthermore, on the basis of calculating the line-descriptor, a hash table and a vote were constructed, thus rough-matching pairs could be obtained. Eventually, mismatches were eliminated by the purification based on matching quality. Experiments were carried out on Mikolajczyk standard image data set of Oxford University. Results show that the proposed improved D-Nets algorithm has an average registration accuracy of 92.2% and an average time cost of 2.48 s under large variation of scale, parallax and light. Compared to Scale-Invariant Feature Transform (SIFT), Affine-SIFT (ASIFT), original D-Nets algorithms, the improved algorithm has a similar registration accuracy with the original algorithm but with up to 80 times speed boost, and it has the best robustness which significantly outperforms SIFT and ASIFT, which is practical for image registration applications.

Triangulation procedure is one of the time bottle-necks of 3D reconstruction system. To increase the speed of triangulation procedure, a parallel Delaunay algorithm was designed based on a shared memory multi-core computer. The algorithm employed divide-and-conquer method and improved conquer procedure and Delaunay mesh optimization procedure to avoid data competition problem. Experiments were conducted on datasets with range from 500000 to 5000000 gathered on the lunar surface simulation ground, and the speedup of the algorithm reached 6.44. In addition, the algorithm complexity and parallel efficiency were fully analyzed and the algorithm was applied in the lunar surface terrain reconstruction system to realize fast virtual terrain reconstruction.

With the rapid development of digital product, the copyright protection of PDF documents has become a crucial research topic in the information security field. This paper analyzed the structure of PDF documents and the relative digital watermarking algorithms. In order to solve the problem that some large capacity text watermarking algorithms increase the size of the document, the authors proposed a digital watermarking algorithm based on the structure of PDF document. Considering the characteristics that end-of-line marker cannot be shown in the document, this algorithm managed to embed watermark through replacing end-of-line markers of the cross-reference table with a fixed format. The experimental results show that, the proposed algorithm can not only reach the standard of watermark capacity required by digital copyright protection, but also has good concealment and can resist statistics attack as well.

Concerning the problems that traditional path planning of group animation needs long time for searching and is of poor optimization, the authors proposed a multi-threaded path planning algorithm based on group search optimization. Firstly, to solve the problem that the algorithm easily gets trapped in local optimum, metroplis rule was introduced in this search mode. Secondly, by using random path through the multi-threading and stitching techniques, the algorithm was applied to path planning. The simulation results show that the algorithm has better global convergence both in high-dimensional and low-dimensional cases, and the method is good enough to meet the requirements of path planning in complex animation environment.

This paper mainly addressed how to identify a mass of dada in NetFlow environment. It proposed an algorithm of abnormal flow identification based on dynamic K-layer feature model. With priority strategies, index table was opened reading the abnormal behavior and matching with eigenvalues one by one. When a match was done successfully, it was marked and the type of abnormal behavior was determined. The experimental result shows that it can identify the abnormal flow quickly and efficiently. It improves the efficiency of identification, and solves network security problems and achieves design goals.

This paper proposed a new Multi-Space Multi-Resolutin Markov Random Field Model (MS-MRMRF). Concerning the inadequate description of the color images in a single RGB space, the proposed model firstly transformed images from the RGB color space to the HSV color space and combined these two color spaces as a multi-space feature; then a new multi-resolution Markov model was designed to segment the image based on the multi-space feature, which estimated the parameters by fuzzy theory. The experiments of the color images demonstrate that the segmentation results of MS-MRMRF model have a higher segmentation accuracy compared with the segmentation results of multi-resolution MRF with a single RGB space.