基于循环神经网络的无线网络入侵检测分类模型构建与优化研究  

Recurrent Neural Networks Based Wireless Network Intrusion Detection and Classification Model Construction and Optimization

作　　者：陈红松[1,2] 陈京九 CHEN Hongsong;CHEN Jingjiu(School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China)

机构地区：[1]北京科技大学计算机与通信工程学院,北京100083 [2]材料领域知识工程北京市重点实验室,北京100083

出　　处：《电子与信息学报》2019年第6期1427-1433,共7页Journal of Electronics & Information Technology

基　　金：国家重点研发计划(2018YFB0803400,2018YFB0803403);国家社科基金(18BGJ071)~~

摘　　要：为提高无线网络入侵检测模型的综合性能,该文将循环神经网络(RNN)算法用于构建无线网络入侵检测分类模型。针对无线网络入侵检测训练数据样本分布不均衡导致分类模型出现过拟合的问题,在对原始数据进行清洗、转换、特征选择等预处理基础上,提出基于窗口的实例选择算法精简训练数据集。对攻击分类模型的网络结构、激活函数和可复用性进行综合优化实验,得到最终优化模型,分类准确率达到98.6699%,综合优化后的运行时间为9.13 s。与其他机器学习算法结果比较,该优化方法在分类准确率和执行效率两个方面取得了很好的效果,综合性能优于传统的入侵检测分类模型。In order to improve the comprehensive performance of the wireless network intrusion detection model, Recurrent Neural Network (RNN) algorithm is used to build a wireless network intrusion detection classification model. For the over-fitting problem of the classification model caused by the imbalance of training data samples distribution in wireless network intrusion detection, based on the pre-treatment of raw data cleaning, transformation, feature selection, etc., an instance selection algorithm based on window is proposed to refine the train data-set. The network structure, activation function and re-usability of the attack classification model are optimized experimentally, so the optimization model is obtained finally. The classification accuracy of the optimization model is 98.6699%, and the running time after the model reuse optimization is 9.13 s. Compared to other machine learning algorithms, the proposed approach achieves good results in classification accuracy and execution efficiency. The comprehensive performances of the proposed model are better than those of traditional intrusion detection model.

关 键 词：入侵检测 循环神经网络 实例选择 模型优化 实验验证 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]