摘要
入侵检测系统的防御性能经常受到类不平衡数据的影响,为了自动提取稀缺类别的数据特征,提高入侵检测系统识别未知网络攻击的精度,提出一种代价约束算法。首先,基于栈式自动编码器构建深度神经网络,在隐藏层的神经元上添加稀疏约束;其次,通过生成代价矩阵优化代价目标函数,对类不平衡数据特征分配代价;最后,利用反向传播微调神经网络模型参数,得到最优的特征向量。仿真结果表明,在面对多维和类不平衡数据时,与FAE算法和NDAE算法相比,代价约束算法在入侵检测精度和收敛性方面均有提升。
The defense performance of intrusion detection system is often affected by class unbalance data.In order to automatically extract data features of scarce categories to improve the accuracy of intrusion detection systems in identifying unknown network attacks,a cost constraint algorithm is proposed.Firstly,a deep neural network based on stacked autoencoder is built up,and sparse constraints on the neurons are added in the hidden layer.Secondly,the cost objective function is optimized by generating a cost matrix,and costs are assigned to imbalanced data features.Finally,the back propagation is used to finely tune the parameters of the neural network model to obtain the optimal feature vector.The simulation results show that,compared with the FAE algorithm and the NDAE algorithm,the cost constraint algorithm improves the intrusion detection accuracy and convergence for multi-dimensional and class imbalanced data.
作者
刘云
郑文凤
张轶
LIU Yun;ZHENG Wen-feng;ZHANG Yi(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)
出处
《计算机工程与科学》
CSCD
北大核心
2022年第3期447-453,共7页
Computer Engineering & Science
基金
国家自然科学基金(61761025)
云南省重大科技专项计划(202002AD080002)。
关键词
入侵检测
特征提取
自动编码器
代价矩阵
深度学习
intrusion detection
feature extraction
autoencoder
cost matrix
deep learning
