基于自监督特征增强的CNN-BiLSTM网络入侵检测方法

针对网络入侵检测中攻击样本和流量特征不足的问题，提出一种基于自监督特征增强的CNN-BiLSTM网络入侵检测方法，实现在流量数据中检测异常网络流量的目标。通过分析流量特征数据分布差异，采用IQR异常值处理方法进行数据预处理，使用自编码器对攻击样本进行数据增强，构建CNN-BiLSTM神经网络和自编码器组成半自监督模型，分别提取高维流量特征和自监督特征，将组合特征作为最终特征输入到分类模型中进行预测分类，实现网络入侵检测。实验结果表明，与其他入侵检测方法相比，所提方法在准确率和F1分数上分别达到了85.7%和85.1%,能够有效提高网络入侵的检测精度以及对未知攻击的检测能力。

CNN-BiLSTM network intrusion detection method based onself-supervised feature enhancement

Aiming at the problem of insufficient attack samples and traffic characteristics in network intrusion detection, a CNN-BiLSTMnetwork intrusion detection method based on self-supervised feature enhancement was proposed to detect abnormal network traffic intraffic data. By analyzing the difference in the distribution of traffic characteristic, IQR outlier processing method was used for datapreprocessing, and autoencoder was used to enhance the number of attack samples. A semi-self-supervised model composed of CNNBilSTM neural network and autoencoder was constructed to extract high-dimensional traffic characteristics and self-supervised featuresrespectively. The combined features are input into the classification model as the final features for prediction and classification, so as torealize the function of network intrusion detection. The experimental results show that compared with other intrusion detection methods,the accuracy and F1 score of the proposed method are 85. 7% and 85. 1% respectively, which can effectively improve the detectionaccuracy of network intrusion and the detection ability of unknown attacks.