吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于数据挖掘的网络状态异常检测

周鹏1, 熊运余2   

  1. 1. 黄淮学院 国际教育学院, 河南 驻马店 463000; 2. 四川大学 计算机学院, 成都 610065
  • 收稿日期:2016-09-12 出版日期:2017-09-26 发布日期:2017-09-26
  • 通讯作者: 周鹏 E-mail:zhoupen0082@126.com

Anomaly Detection of Network State Based on Data Mining

ZHOU Peng1, XIONG Yunyu2   

  1. 1. College of International, Huanghuai University, Zhumadian 463000, Henan Province, China;2. College of Computer Science, Sichuan University, Chengdu 610065, China
  • Received:2016-09-12 Online:2017-09-26 Published:2017-09-26
  • Contact: ZHOU Peng E-mail:zhoupen0082@126.com

摘要: 针对目前网络状态异常行为检测正确率低的问题, 提出一种基于数据挖掘的网络状态异常检测模型. 首先提取网络状态信号, 通过小波变换对信号进行预处理, 并提取网络状态异常检测的特征; 然后通过回声状态网络对网络状态异常检测进行建模, 并通过遗传算法对回声状态网络的参数进行优化; 最后采用网络状态异常数据集对模型的有效性进行测试. 测试结果表明, 数据挖掘技术可以准确检测各种网络状态异常行为.

关键词: 数据挖掘, 检测模型, 入侵行为, 网络异常

Abstract: Aiming at the problem of low detection accuracy for abnormal behavior of network states, we proposed an anomaly detection model  of network state based on data mining. Firstly, the network state signal was extracted, and the signal was pretreated by wavelet transform, and the features of the network
 anomaly detection were extracted. Secondly, the network state anomaly detection model was built by echo state network, and genetic algorithm was used to optimize the parameters of the echo state network. Finally, the network state anomaly data sets were used to test the effectiveness of the model. The test results show that data mining technology can accurately detect abnormal behavior of various network states.

Key words:  network anomaly, intrusion behavior, detection model, data mining

中图分类号: 

  • TP391