基于ADASYN与改进残差网络的入侵流量检测识别

针对现有入侵流量检测模型分类准确率低、小样本特征提取不足等问题, 提出了一种基于自适应合成采样和Inception-Resnet模块的改进残差网络算法。该算法能够对不平衡数据集进行采样优化, 有效提升模型的小样本特征提取能力。首先, 通过对不平衡的数据训练集进行过采样改善数据分布, 然后对非数据部分进行独热编码处理并与数据部分整合, 降低预处理复杂度, 最后利用改进残差网络模型进行数据训练, 并进行性能评估和算法效能对比。实验结果表明, 改进残差网络模型对入侵流量的检测准确率在多分类和二分类情况下分别达到89.40%和91.88%。相比于经典深度学习算法, 改进残差网络模型的准确率更高, 误报率更低, 具备较高的可靠性和工程应用价值。

Intrusion traffic detection and identification based on ADASYN and improved residual network

To solve the problems of low classification accuracy and insufficient feature extraction of small samples in existing intrusion traffic detection models, an improved residual network algorithm based on adaptive synthetic (ADASYN) sampling and Inception-Resnet modules is proposed. The algorithm can optimize the unbalanced data set and improve the feature extraction ability of small sample effectively. Firstly, the unbalanced data training set is oversampled to improve the data distribution, and then the non-data part is processed and integrated with the data part to reduce the complexity of pretreatment. Finally, the improved residual network model is used to train the data, and the performance evaluation and algorithm efficiency comparison are carried out. The experimental results show that the detection accuracy of intrusion traffic by the improved residual network model reaches 89.40% and 91.88% respectively in the case of multi-classification and binary classification. Compared with the classical deep learning algorithm, the improved residual network model has higher accuracy and lower false alarm rate, which has higher reliability and engineering application value.