| 基于机器学习与大数据技术的入侵检测方法研究 |
投稿时间：2021-09-01 修订日期：2021-10-25  点此下载全文 |
| 引用本文： |
| 摘要点击次数: 93 |
| 全文下载次数: 0 |
|
|
| 中文摘要:为了维护良好的网络环境,保障人们的利益,解决网络安全问题,提出基于机器学习与大数据技术的入侵检测方法,首先分析了当前网络入侵检测算法研究进展,描述了大数据分析技术的网络入侵原理,然后将GRU神经网络与SVM分类算法相结合,提高分类精度,最后选择当前标准的网络入侵检测数据集进行仿真实验。实验结果表明基于GRU-SVM模型的网络入侵检测成功率相当高,网络入侵行为的漏检率与误检率明显降低,相对于其它检测模型,基于GRU-SVM模型的网络入侵检测整体效果得到了有效改善,可以保证网络安全。 |
| 中文关键词:网络安全 机器学习 大数据技术 入侵检测 |
| |
| Research on Intrusion Detection Method Based on Machine Learning and Big Data TechnologyRen Shou dong1,Chen Liang2,Tong Xiao tong3,Li Hui yan4,Zhang Jing5 |
|
|
| Abstract:In order to maintain a good network environment, protect people"s interests, and solve network security problems, an intrusion detection method based on machine learning and big data technology is proposed. First, the current research progress of network intrusion detection algorithms is analyzed, and the network intrusion of big data analysis technology is described. Principle, then combine the GRU neural network with the SVM classification algorithm to improve the classification accuracy, and finally select the current standard network intrusion detection data set for simulation experiments. The experimental results show that the success rate of network intrusion detection based on the GRU-SVM model is quite high, and the missed detection rate and false detection rate of network intrusion behaviors are significantly reduced. Compared with other detection models, the overall effect of network intrusion detection based on the GRU-SVM model has been obtained. Effective improvement can ensure network security. |
| keywords:Network security Machine learning Big data technology Intrusion detection |
| 查看全文 查看/发表评论 下载pdf阅读器 |
|
|
|
次