In Software Defined Network (SDN), Dynamic Host Configuration Protocol (DHCP) flood attack packets can actively enter the controller in reactive mode, which causes a huge hazard to SDN. Aiming at the promblem that the traditional defense method against DHCP flood attack cannot keep the SDN network from control link blocking caused by the attack, a Dynamic Defense Mechanism (DDM) against DHCP flood attacks was proposed. DDM is composed of a detection model and mitigation model. In the detection model, different from the static threshold detection method, a dynamic peak estimation model was constructed by two key parameters - DHCP average traffic seed and IP pool surplus to evaluate whether the ports were attacked. If the ports were attacked, the mitigation model would be informed. In the mitigation model, the IP pool cleaning was performed based on the response character of Address Resolution Protocol (ARP), and an interval interception mechanism was designed to intercept the attack source, mitigating the congestion and minimizing the impact on users during interception. Simulation experimental results show that the detection error of DDM is averagely 18.75%, lower than that of the static threshold detection. The DDM mitigation model can effectively intercept traffic and reduce the waiting time for users to access the network during the interception by an average of 81.45%.

As the limitation of single feature representation may cause low accuracy of fine-grained image classification, a multi-feature combination representation method based on Convolutional Neural Network (CNN) and Scale Invariant Feature Transform (SIFT) was proposed. The features were extracted from the entire target, the key parts and the key points comprehensively. Firstly, two CNN models were trained with the target-entirety regions and the head-only regions in the fine-grained image library respectively, which were used to extract the target-entirety and the head-only CNN features. Secondly, the SIFT key points were extracted from all the target-entirety regions in the image library, and the codebook was generated through the K-means clustering. Then, the SIFT descriptors of each target-entirety region were encoded into a feature vector by using the Vector of Locally Aggregated Descriptors (VLAD) along with the codebook. Finally, Support Vector Machine (SVM) was used to classify the fine-grained images by using the combination of multiple features. The method was evaluated in CUB-200-2011 database and compared with the single feature representation method. The experimental results show that the proposed method can improve the classification accuracy by 13.31% compared with the single CNN feature representation, which proves the positive effect of multi-feature combination on fine-grained image classification.

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.