淮阴师范学院学报(自然科学版）

针对小样本环境下已有入侵数据特征提取算法存在的训练性能差、检测误报率高等问题，设计了一种融合残差网络的特征提取算法.首先对小样本数据进行预处理，降低输入数据的维数；其次利用残差模块堆栈构成卷积层，提升模型的数据训练能力和对小样本数据的特征提取能力，基于长短时记忆网络模型的门控机制，实时调整和控制输入数据特征的流量损失；最后采用最小目标函数法优化最终的特征分类结果.实验结果显示：融合残差网络的数据集检测时间短，在60次内可完成迭代，针对不同类型攻击的检测精确率均值在97%以上.

Aiming at the problems of poor training performance and high false positive detection rate of existing intrusion data feature extraction algorithms in small sample environment, a feature extraction algorithm based on fusion residual network is designed. Firstly, the small sample data is preprocessed to reduce the dimension of the input data. Secondly, the residual module stack is used to form a convolutional layer to improve the data training ability of the model and the feature extraction ability of small sample data. Based on the gating mechanism of the short-time memory network model, the flow loss of input data features is adjusted and controlled in real time. Finally, the minimum objective function method is used to optimize the final feature classification results. The experimental results show that the data set detection time of fusion residual network is short, iteration can be completed within 60 times, and the detection accuracy rate for different types of attacks is more than 97%.