结合高斯混合模型与排序学习的多阶段入侵检测方法

• Format: Patent
• Language: Chinese
• Published: 17.06.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC COMMUNICATION TECHNIQUE; ELECTRIC DIGITAL DATA PROCESSING; ELECTRICITY; PHYSICS; TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION

结合高斯混合模型与排序学习的多阶段入侵检测方法，S1获取恶意入侵流量数据得到网络流量特征数据集；S2将网络流量特征数据集进行划分并提取最优特征；S3对先验信息集进行预测得到错误分类样本和重叠样本分布情况；S4将错误样本与最优特征测试集进行特征值匹配，通过模型预测得到第一阶段预测结果；S5结合错误样本分布情况，得到重叠样本与非重叠样本，以重叠样本的先验信息为该重叠样本制定预测标签，得到第二阶段预测结果；S6对非重叠样进行分类预测得到第一拼接向量；S7将第一拼接向量通过排序学习模型预测，得到第三阶段预测结果；结合高斯混合模型以及排序学习解决同特征不同标签的样本以及类别易混淆样本分类效果差的问题。 The invention discloses a multi-stage intrusion detection method combining a Gaussian mixture model and rank learning. The method comprises the following steps: S1, acquiring malicious intrusion traffic data to obtain a network traffic feature data set; S2, dividing the network traffic feature data set and extracting optimal features; S3, predicting the prior information set to obtain distribution conditions of misclassification samples and overlapped samples; S4, carrying out feature value matching on the error sample and the optimal feature test set, and obtaining a first-stage prediction result through model prediction; S5, obtaining the overlapped samples and non-overlapped samples in combination with the error