陈京九1
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
基金项目:国家重点研发计划(2018YFB0803400, 2018YFB0803403)，国家社科基金(18BGJ071)
详细信息
作者简介:陈红松：男，1977年生，副教授，研究方向为网络空间安全、大数据与机器学习算法应用、云计算与物联网安全
陈京九：男，1994年生，硕士生，研究方向为网络空间安全
通讯作者:陈红松 chenhs@ustb.edu.cn
中图分类号:TP393.08; TP183计量
文章访问数:1452
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被引次数:0
出版历程
收稿日期:2018-07-10
修回日期:2019-01-07
网络出版日期:2019-01-18
刊出日期:2019-06-01
Recurrent Neural Networks Based Wireless Network Intrusion Detection and Classification Model Construction and Optimization
Hongsong CHEN1, 2,,,Jingjiu CHEN1
1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China
Funds:The National Key Research Development Program (2018YFB0803400, 2018YFB0803403), The National Social Science Foundation of China (18BGJ071)
摘要
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。
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
Abstract: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.
Key words:Intrusion detection/
Recurrent Neural Network (RNN)/
Instance selection/
Model optimization/
experimental verification
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