用于入侵检测的长短期记忆递归神经网络分类器

Jihyun Kim, Jaehyun Kim, Huong Le Thi Thu, Howon Kim
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引用次数: 467

摘要

由于信息和通信技术的进步,通过网络分享信息的情况有所增加。这就产生了新的附加价值。因此,各种在线服务应运而生。然而,随着越来越多的人连接到互联网,网络安全的威胁也在增加。入侵检测系统(IDS)是当今重要的安全问题之一。在本文中,我们用深度学习的方法构造了一个IDS模型。我们将长短期记忆(LSTM)架构应用于递归神经网络(RNN),并使用KDD Cup 1999数据集训练IDS模型。通过性能测试,我们证实了深度学习方法对IDS是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection
Due to the advance of information and communication techniques, sharing information through online has been increased. And this leads to creating the new added value. As a result, various online services were created. However, as increasing connection points to the internet, the threats of cyber security have also been increasing. Intrusion detection system(IDS) is one of the important security issues today. In this paper, we construct an IDS model with deep learning approach. We apply Long Short Term Memory(LSTM) architecture to a Recurrent Neural Network(RNN) and train the IDS model using KDD Cup 1999 dataset. Through the performance test, we confirm that the deep learning approach is effective for IDS.
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