Currently， the network security accidents occur frequently， and traditional passive defense technologies have no possible response to unknown network security threats. In response to this problem， a multi-stage evolutionary signal game model was constructed. And with the background that the defender actively launches inductive signals for security defense， a Moving Target Defense （MTD） decision-making algorithm based on the multi-stage evolutionary signal game model was proposed. Firstly， the basic elements of the model were defined and the overall model was analyzed theoretically based on the assumptions of incomplete information constraints and complete rationality of both sides of the game. Then， a method for quantifying the benefits of offensive and defensive strategies was designed， and a detailed optimal strategy solving process for equilibrium was given. Finally， the MTD method was introduced to analyze the evolution trends of both sides’ strategies in multi-stage attack and defense. Experimental results show that the proposed algorithm can predict the optimal defense strategies at different stages accurately， and has guiding significance for the research of new network active defense technology. At the same time， the results of comparing the proposed algorithm with the traditional random uniform strategy selection algorithm through Monte Carlo simulation experiment verify the effectiveness and safety of the proposed algorithm.

In Software Defined Network （SDN）， independent development of various network applications and multi-user network management may cause conflicts in the flow rules issued to switching equipment. Due to the separation of the control plane and the forwarding plane， the switching equipments lack strategy analysis capability， and cannot independently detect internal flow rule conflicts. Aiming at this problem， a flow rule conflict detection system and a detection algorithm were proposed. Firstly， by monitoring and capturing OpenFlow messages between the control plane and the forwarding plane， the information about the flow rules to be issued was obtained. Then， the conflict detection algorithm was used to determine the conflict type of the flow rules. The corresponding rule set was selected by the algorithm according to the matching protocol of flow rules， thereby reducing the detection scale. In the detection， the features of Non-Conflict （NC） rules were detected at first， so that the detection efficiency of NC rules was higher than those of other types of conflict rules. Finally， the flow rule conflicts were resolved according to the conflict types. Experimental results show that the detection accuracy of the proposed algorithm can reach 100%； compared with the dynamic conflict detection model， the proposed algorithm shortens the detection time by about 47% under the same scale of rule set. And the detection time is shortened as the proportion of NC rules increases.