The survivability of defense system refers to the ability of the system to maintain or restore its defense performance to an acceptable level when the system is facing enemy attack damage or its own node failure. In order to measure and characterize the survivability of defense systems with different architectures， based on the combat confrontation scenario and from the ability of the system to build strike links， a new method of survivability measurement of defense systems is proposed. This method can be used to quantitatively characterize the survivability of traditional defense system and distributed defense system， and provide support for further distributed defense research and high survivability architecture design of defense system.

Aiming at the lack of automated and intelligent forensics methods in the aviation reconnaissance and evidence collection， an aviation forensics target detection algorithm based on the fusion of RetinaNet and SE is proposed to solve the problem of large changes in target scale and imbalance in data collection. The performance is further improved by introducing an attention mechanism through the SE （squeeze-excitations） module. The feature pyramid network（FPN） can effectively deal with the problem of large changes in target scale， Focal Loss can effectively deal with the imbalance of categories in dataset， the SE module introduces a channel attention mechanism to strengthen the feature map， and can further use the extracted channel correlation to enhance effective features and suppress ineffective features. Through simulation experiments， it is verified that the algorithm can improve the accuracy of target detection with a small amount of calculation increased， further enhance the characterization ability of the model， and effectively improve the efficiency of target detection， which can provide a reference for related engineering applications.