To solve the high storage cost and low retrieval speed problems in malicious Structure Query Language (SQL) detection faced by Nearest Neighbor (NN) method, a Hash learning based Malicious SQL Detection (HMSD) method was proposed. In this algorithm, Hash learning was used to learn the binary coding representation for SQL statements. Firstly, the SQL statements were presented as real-valued features by washing and deleting the duplicated SQL statements. Secondly, the isotropic hashing was used to learn the binary coding representation for SQL statements. Lastly, the retrieval procedure was performed and the detection speed was improved by using binary coding representation. Experimental results show that on the malicious SQL detection dataset Wafamole, the dataset is randomly divided so that the training set contains 10 000 SQL statements and the test set contains 30 000 SQL statements, at the length of 128 bits, compared with nearest neighbor method, the proposed algorithm has the detection accuracy increased by 1.3%, the False Positive Rate (FPR) reduced by 0.19%,the False Negative Rate (FNR) decreased by 2.41%, the retrieval time reduced by 94%, the storage cost dropped by 97.5%; compared with support vector machine method, the proposed algorithm has the detection accuracy increased by 0.17%, which demonstrate that the proposed algorithm can solve the problems of nearest neighbor method in malicious SQL detection.

In order to solve the problem that the query efficiency of distributed database decreases with the increase of data scale, the Greenplum distributed database was taken as the research object, and a cost-based optimal query plan generation scheme was proposed from the perspective of optimizing the query path. Firstly, an effective cost model was designed to estimate the query cost. The parallel maximum and minimum ant colony algorithm was then used to search the join order with the minimum query cost, i.e. the optimal join order. Finally, the optimal query plan was obtained based on the Greenplum database's default optimal choice for different operations in the query plan. Multiple experiments were carried out on the self-generated data set and Transaction Processing Performance Council Benchmark H (TPC-H) standard data set by using the proposed scheme. The experimental results show that the proposed optimization scheme can effectively search out the optimal solution and obtain the optimal query plan, so as to improve the query efficiency of Greenplum database.

To address the poor results of foreground extraction from dynamic background, a motion detection method based on deep auto-encoder networks was proposed. Firstly, background images without containing motion objects were subtracted from video frames using a three-layer deep auto-encoder network whose cost function contained background as variable. Then, another three-layer deep auto-encoder network was used to learn the subtracted background images which are obtained by constructed separating function. To achieve online motion detection through deep auto-encoder learning, an online learning method of deep auto-encoder network was also proposed. The weights of network were merged according to the sensitivity of cost function to process more video frames. From the experimental results, the proposed method obtains better motion detection accuracy by 6%, and lower false rate by 4.5% than Lus work (LU C, SHI J, JIA J. Online robust dictionary learning. Proceeding of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Piscataway: IEEE Press, 2013:415-422). This work also obtains better extraction results of background and foreground in real applications, and lays better basis for video analysis.