计算机与现代化

• 信息安全 • 上一篇    下一篇

基于异卷积神经网络的入侵检测

  

  1. (国网江苏省电力有限公司苏州供电分公司,江苏苏州215004)
  • 收稿日期:2019-03-19 出版日期:2019-10-28 发布日期:2019-10-29
  • 作者简介:李荷婷(1990-),女,江苏常州人,助理工程师,硕士,研究方向:信息安全,E-mail: loading.527@163.com; 冯仁君(1989-),男,江苏盐城人,硕士,研究方向:软件智能化,信息安全,E-mail: frj1989@126.com; 陈海雁(1974-),男,江苏苏州人,工程师,硕士,研究方向:信息安全,E-mail: 13372175016@189.cn; 景栋盛(1981-),男,江苏苏州人,高级工程师,硕士,研究方向:信息安全,E-mail: jds19810119@163.com。
  • 基金资助:
    江苏省高等学校自然科学研究重大项目(17KJA520004)

Intrusion Detection Based on Heterogeneous Convolutional Neural Network

  1. (Suzhou Power Supply Branch, State Grid Jiangsu Electric Power Limited Company, Suzhou 215004, China)
  • Received:2019-03-19 Online:2019-10-28 Published:2019-10-29

摘要: 网络已经深入人们生产生活的各领域。然而,由于存在大量的非法入侵行为,网络所面临的安全问题也越来越严峻。因此,检测入侵以保障网络安全是一个亟待解决的问题。针对此,本文提出一种基于异卷积神经网络的入侵检测方法,采用深度学习的卷积神经网络模型完成对入侵数据的特征提取,然后根据2种不同结构的卷积神经网络训练数据,从而得到最优模型,用以判断网络入侵。最后,使用KDD 99数据进行对比实验,验证本文方法的准确性和精确性。

关键词: 深度学习, 卷积神经网络, 异卷积神经网络, 入侵检测, 网络安全

Abstract: Network has penetrated into all fields of people’s production and life. However, due to the existence of a large number of illegal intrusions, the network is facing more and more serious security problems. Therefore, detecting intrusion to ensure network security is an urgent problem to be solved. In order to solve this problem, an intrusion detection method based on heterogeneous convolution neural network is proposed. The convolution neural network model of deep learning is used to extract the intrusion data features. Then the optimal model is obtained according to the training data of convolution neural network with two different structures, which can be used to judge the network intrusion. Finally, experiment on KDD 99 verifies the accuracy and accuracy of the method proposed in this paper.

Key words: deep learning, convolutional neural network, heterogeneous convolutional neural network, intrusion detection, network security

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