Aiming at the low efficiency of linear combination of base classifiers and over-adaptation of the traditional AdaBoost algorithm, an improved algorithm based on coefficients and diversity of base classifiers - WD AdaBoost (AdaBoost based on Weight and Double-fault measure) was proposed. Firstly, according to the error rates of the base classifiers and the distribution status of the sample weights, a new method to solve the base classifier coefficients was given to improve the combination efficiency of the base classifiers. Secondly, the double-fault measure was introduced into WD AdaBoost algorithm in the selection strategy of base classifiers for increasing the diversity among base classifiers. On five datasets of different actual application fields, compared with the traditional AdaBoost algorithm, CeffAda algorithm uses the new base classifier coefficient solution method to make the test error reduced by 1.2 percentage points on average; meanwhile, WD AdaBoost algorithm has the lower error rate compared with WLDF_Ada, AD_Ada (Adaptive to Detection AdaBoost), sk_AdaBoost and other algorithms. Experimental results show that WD AdaBoost algorithm can integrate base classifiers more efficiently, resist overfitting, and improve the classification performance.

PICO proposed in recent years is a bit-based ultra lightweight block cipher algorithm. The security of this algorithm to resist integral cryptanalysis was evaluated. Firstly, by analyzing the structure of PICO cipher algorithm, a Mixed-Integer Linear Programming (MILP) model of the algorithm was established based on division property. Then, according to the set constraints, the linear inequalities were generated to describe the propagation rules of division property, and the MILP problem was solved with the help of the mathematical software, the success of constructing the integral distinguisher was judged based on the objective function value. Finally, the automatic search of integral distinguisher of PICO algorithm was realized. Experimental results showed that, the 10-round integral distinguisher of PICO algorithm was searched, which is the longest one so far. However, the small number of plaintexts available is not conducive to key recovery. In order to obtain better attack performance, the searched 9-round distinguisher was used to perform 11-round key recovery attack on PICO algorithm. It is shown that the proposed attack can recover 128-bit round key, the data complexity of the attack is 2 ^63.46, the time complexity is 2 ⁷⁶ 11-round encryptions, and the storage complexity is 2 ²⁰.

Swarm intelligence labor division refers to any algorithm and distributed problem solving method that is inspired by the collective behaviors of social insects and other animal groups. It can be widely used in real-life task assignment. Focusing on the task assignment problem like traffic signal timing, the theory of labor division that describes the interaction mode between bee individuals was introduced, a Bee colony Double Inhibition Labor Division Algorithm (BDILDA) based on swarm intelligence was proposed, in which the dynamic accommodation of swarm labor division was achieved through interaction between internal and external inhibitors of the individual. In order to verify the validity of BDILDA, the traffic signal timing problem was selected for simulation experiments. BDILDA was used to solve actual case of traffic signal timing and the result was compared with the results of Webster algorithm, Multi-Colony Ant Algorithm (MCAA), Transfer Bees Optimizer (TBO) algorithm and Backward FireWorks Algorithm (BFWA). The experimental results show that average delay time of BDILDA is reduced by 14.3-20.1 percentage points, the average parking times is reduced by 3.7-4.5 percentage points, the maximum traffic capacity is increased by 5.2-23.6 percentage points. The results indicate that the proposed algorithm is suitable for solving dynamic assignment problems in uncertain environment.

Concerning the problem that the network access control of Virtual Machines (VM) in the cloud computing Infrastructure as a Service (IaaS) platforms, a method of communication access control for VM in IaaS platforms was proposed. The method based on Software Defined Networking (SDN) was realized to customize the communication access control rules from Layer 2 to Layer 4. The experimental results show that the method can manage communication access permissions of tenants' VM flexibly, and ensure the security of tenants' network.

Bad Mouthing and Self-Promoting (BS) collusion attack group and its detection mechanism, called BSCD, were proposed to resolve the security issues of the multiple malicious node collusion attack network nodes and affect their accurate positioning in the Wireless Sensor Network (WSN) reputation system. And the implementation method of the mechanism was given. It detected the abnormal recommended node, analyzed the evaluation behavior similarity between recommended nodes, and effectively detected the existence of collusion attack group, thereby reduced its damage and impact on the reputation of the system. The simulation results show that, BSCD has significant effect on the detection and resisting BS collusion attack group, effectively improves the malicious node detection rate in the reputation system and the capacity of the entire system to resist malicious node.

A new low-power localization algorithm based on the evaluation of distributed reputation was proposed to improve the security and energy consumption of the node positioning for wireless sensor network. The concepts of Trustworthy Node Table (TNT) and the backup cluster head node were introduced to find the reliable beacon nodes quickly, and the backup cluster head node could assist and monitor the cluster head node, reducing the workload of the cluster head and participating in the integration process of the beacon nodes reputation values. The proposed algorithm enhanced the reliability and integrity of the beacon nodes, improved the efficiency and security of the node localization, reduced the systems energy consumption and improved the detection rate of malicious nodes. The simulation results show that in malicious node environment, the algorithm can effectively improve the detection rate of malicious nodes, reduce the positioning error, weaken the malicious nodes damage and influence on the positioning system to achieve the safe positioning of the nodes.

After qualitative discussion of the synchronization performance in complex network models, simulation analysis of the networks with relatively larger size was presented. Through data analysis, network topology visualization, and topology evolution with simulated annealing algorithm, some rules of synchronization optimization were found, that is, making the degree distribution and average distance uniform and centralized, and proper clustering coefficient can reduce network connection without influencing synchronization. Considering the situation of future power grid, optimization strategies for the stability of synchronization were developed and tested on the data of the actual power grid, exploring the application value of optimizing practical networks from the angle of topology and satisfying the requirement of real-time quality, stability and distribution. The optimization is proved to be effective.

Carving surface was directly modeled using the quadratic Bezier curve surface according to the grey or color pixels information of the image with clear texture and simple construction. This method segmented the image, analyzed and computed the pixel values and their position parameters for the sub-images, and used those values as feature parameters for constructing the surface patches of the model. The modeling was implemented by limited smooth connection of surface patches. The established surface model can describe the basic structure characteristics of the original objects in the image, and satisfy the requirements of digital carving processing. It has low complexity and strong practicability.