Unmanned Aerial Vehicle （UAV） swarm path planning and task allocation are the cores of UAV swarm rescue applications. However， traditional methods solve path planning and task allocation separately， resulting in uneven resource allocation. In order to solve the above problem， combined with the physical attributes and application environmental factors of UAV swarm， the Ant Colony Optimization （ACO） was improved， and a Joint Parallel ACO （JPACO） was proposed. Firstly， the pheromone was updated by the hierarchical pheromone enhancement coefficient mechanism to improve the performance of JPACO task allocation balance and energy consumption balance. Secondly， the path balance factor and dynamic probability transfer factor were designed to optimize the ant colony model， which is easy to fall into local convergence， so as to improve the global search capability of JPACO. Finally， the cluster parallel processing mechanism was introduced to reduce the time consumption of JPACO operation. JPACO was compared with Adaptive Dynamic ACO （ADACO）， Scanning Motion ACO （SMACO）， Greedy Strategy ACO （GSACO） and Intersecting ACO （IACO） in terms of optimal path， task allocation balance， energy consumption balance and operation time on the open dataset CVRPLIB. Experimental results show that the average value of the optimal paths of JPACO is 7.4% and 16.3% lower than of IACO and ADACO respectively in processing small-scale operations. Compared with GSACO and ADACO， JPACO has the solution time reduced by 8.2% and 22.1% in large-scale operations. It is verified that JPACO can improve the optimal path when dealing with small-scale operations， and is obviously superior to the comparison algorithms in terms of task allocation balance， energy consumption balance， and operation time consumption when processing large-scale operations.

In order to avoid the problem of blind discretization of traditional classification "discretize first learn second", a new method of associative classification based on lazy thought was proposed. It discretized the new training dataset gotten by determining the K-nearest neighbors of test instance firstly, and then mined associative rules form the discrete dataset and built a classifier for predicting the class label of test instance. At last, the results of contrastive experiments with CBA (Classification Based on Associations), CMAR (Classification based on Multiple Class-Association Rules) and CPAR (Classification based on Predictive Association Rules) carried out on seven commonly used quantitative datasets of UCI show that the classification accuracy of the proposed method can be increased by 0.66% to 1.65%, and verify the feasibility of this method.

Group key agreement is one of the important stages to carry out secure multicast communication. A group controller node switch method was given pointing to the problem of satellite network topology changed dynamically. It could adjust controlling nodes in a dynamic way. Then, both authentication and integrality mechanism were used to attest agreement messages and group keys, a group key generation and renewing method was proposed, which could improve security of agreement messages. The results of simulation and analysis show that this group key agreement protocol leads to high efficiency and security.

According to the architecture of multi-core processors and the temporal local characteristics of network data flow, a division and dynamic adaptation algorithm was proposed based on multi-core processors. Classifying network data flow by the type and optimizing chain of rules dynamically by the temporal locality of network flow, the count of the multi-core's L7-Filter matching network data flow were reduced effectively and the processing efficiency was improved dramatically. The simulation result shows that given the number of packets in the same conditions, the algorithm has about 7 percent improvement of the multi-core processing performance. With the increasing number of network packets, the performance superiority becomes more obvious.

Security routing protocol is a key element to guarantee satellite network security. To solve the problem that most of routing protocols lack security scheme, the Elliptic Curve Pintsov-Vanstone Signature Scheme (ECPVSS) was used to attain confidentiality and authentication of packets, and trust evaluation scheme could exclude internal malicious node from the route path. Then a security routing protocol oriented to High Altitude Platform (HAP)/Low Earth Orbit (LEO) architecture was formed. The analysis results show that the proposed protocol can prevent network from some common routing attacks.

A risk assessment model based on Bayesian network was proposed. In this model, each risk event influencing the Trusted Platform Control Module (TPCM)'s trust was analyzed. According to the relation among risks, the Bayesian network evaluation model was built. According to the evaluation from expert, Bayesian network inferring method was used to evaluate the risk probability and its influence. The whole system's risk value and risk priority were determined. An example was given to verify the model's correctness and validation.

Aiming at the problem of equivalent conversion from conjunctive normal form to disjunctive normal form, an effective algorithm was proposed to construct reduction tree based on discernibility matrix. The discernibility set was acquired by improving discernibility matrix, and then reduction tree was designed to describe concrete process of getting reductions, finally each path from root to leaf was set to a reduction. This method decreases overhead in forming and storing discernibility matrix, and simplifies the process of acquiring all reductions of decision system.