基于DNN-FM模型的网络入侵检测研究Research on network intrusion detection based on DNN-FM model
张全龙;王怀彬;
摘要(Abstract)：
与随着网络技术的飞速发展,主动防御网络入侵比以往更加重要.误报率高和检测率低的主要原因之一是不能很好的对数据集间的特征进行交互学习.在本文中,我们提出了一种可以对低阶和高阶特征进行交互学习的模型.模型DNN-FM在新的神经网络体系结构中结合了因子分解机和深度神经网络对低阶和高阶特征进行交互学习.在KDD99数据集上进行了实验之后,证明DNN-FM模型与现有网络入侵检测模型相比,有更高的检测率.
关键词(KeyWords)： 网络入侵检测;深度神经网络;因子分解机;DNN-FM;特征交互
基金项目(Foundation): 天津科技重大专项(16YDLJGX00210)
作者(Author): 张全龙;王怀彬;
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DOI:
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