Construction and Application of Machine Learning Model in Network Intrusion Detection

Qi Guanglei, Chen Zhijiang, Zhao Haiying, Wu Chensheng
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Abstract

The modeling of network intrusion detection is an important network security protection technology. The current network intrusion detection model can not accurately describe the intrusion behavior, resulting in incomplete network intrusion detection. Therefore, a network intrusion detection model based on machine learning algorithm was designed. In addition, support vector machine (SVM) fits the mapping relationship between network intrusion detection characteristics and network intrusion behavior, and established a network intrusion detection model that reflected the relationship between the two aspects. Finally, the experimental results showed that the model not only can accurately identify the network intrusion behavior, but also has a very fast detection speed. It has obtained better network intrusion detection results than other models, and had a wide application prospect. Keywords-Network Intrusion Detection; Machine Learning; SVM; Network Security
机器学习模型在网络入侵检测中的构建与应用
网络入侵检测建模是一项重要的网络安全防护技术。现有的网络入侵检测模型不能准确地描述入侵行为,导致网络入侵检测不完整。为此,设计了一种基于机器学习算法的网络入侵检测模型。此外,支持向量机(SVM)拟合了网络入侵检测特征与网络入侵行为之间的映射关系,建立了反映两者关系的网络入侵检测模型。最后,实验结果表明,该模型不仅能够准确识别网络入侵行为,而且具有非常快的检测速度。该模型取得了较好的网络入侵检测效果,具有广阔的应用前景。关键词:网络入侵检测;机器学习;支持向量机;网络安全
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