基于机器学习与大数据技术的入侵检测方法研究
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引用本文:任守东 1 ,陈 亮 2 ,佟晓童 1 ,李绘妍 1 ,张 晶 1.基于机器学习与大数据技术的入侵检测方法研究[J].计算技术与自动化,2022,(3):172-175
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任守东 1 ,陈 亮 2 ,佟晓童 1 ,李绘妍 1 ,张 晶 1 (1.国网抚顺供电公司辽宁 抚顺 1130082.国网辽宁省电力有限公司辽宁 沈阳 110006) 
中文摘要:为有效检测网络的攻击行为,提出了基于机器学习与大数据技术的入侵检测方法。首先分析当前网络入侵检测算法,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,最后使用网络入侵检测数据集进行实验。实验结果表明基于GRU-SVM模型的网络入侵检测成功率高于其他模型,网络入侵检测整体效果得到改善,保证了网络安全。
中文关键词:网络安全  机器学习  大数据技术  入侵检测
 
Intrusion Detection Method Based on Machine Learning and Big Data Technology
Abstract:In order to effectively detect network attacks, an intrusion detection method based on machine learning and big data technology is proposed. First, the current network intrusion detection algorithm is analyzed, and the network intrusion principle of big data analysis technology is described, and then the GRU neural network and the SVM classification algorithm are combined. And finally the network intrusion detection data set is used for experiment. Experimental results show that the success rate of network intrusion detection based on GRU-SVM model is higher than other models, the overall effect of network intrusion detection is improved, and network security is guaranteed.
keywords:network security  machine learning  big data technology  intrusion detection
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