一种基于LMDR和CNN的混合入侵检测模型

A Hybrid Model of Intrusion Detection Based on LMDR and CNN

随着网络安全技术的飞速发展和大数据技术的广泛应用,传统的机器学习模型已难以满足大数据环境下高效入侵检测的要求。针对原始数据集特征不够明显的情况,利用卷积神经网络进行大数据特征提取与数据分析的优势,文章提出一种基于对数边际密度比(Logarithm Marginal Density Ratio,LMDR)和卷积神经网络(Convotional Neural Network,CNN)的混合入侵检测模型。该模型相较于现有传统的机器学习算法和神经网络模型,能够更充分挖掘数据特征间的联系,有效提高分类准确率并降低误报率。

With the rapid development of network security technology and the big data technology,the traditional machine learning model has been difficult to meet the requirements of efficient intrusion detection in big data environment.For this reason,considering the advantages of convolutional neural network in feature extraction and data analysis,this paper proposed a mixed intrusion detection model based on logarithm marginal density ratio and convolutional neural network in view of the fact that the characteristics of the original dataset was not obvious enough.Compared with the traditional machine learning algorithm and neural network model,our hybrid model can make full use of the relationship between features for feature enhancement,and effectively improve the classification accuracy and reduce the false alarm rate.