当前位置: X-MOL 学术Mobile Netw. Appl. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Robust Network Intrusion Detection Scheme Using Long-Short Term Memory Based Convolutional Neural Networks
Mobile Networks and Applications ( IF 3.8 ) Pub Date : 2020-07-21 , DOI: 10.1007/s11036-020-01623-2
Chia-Ming Hsu , Muhammad Zulfan Azhari , He-Yen Hsieh , Setya Widyawan Prakosa , Jenq-Shiou Leu

The intrusion detection system (IDS) is a crucial part in the network administration system to detect some types of cyber attack. IDS is categorized as a classifying machine thus it is likely to engage with the machine learning schemes. Many studies have demonstrated how to apply machine learning schemes to IDS even though they cannot provide optimum results. To tackle this issue, deep learning schemes can be considered as the solution due to its achievement in several fields. Therefore, in this study, we propose a deep learning model which is constructed based on convolutional neural network (CNN) layers and using Long-Short Term Memory (LSTM) layers called CNN-LSTM to classify every single traffic network. We use NSL-KDD dataset as the benchmark thus we can compare the performance of our proposed method with other existing works. This dataset includes two testing sets which are the first one is KDDTest+ while the second one is KDDTest− 21 which is more difficult to be classified. The results show that our proposed method outperforms other existing works.



中文翻译:

基于长时记忆的卷积神经网络的鲁棒网络入侵检测方案

入侵检测系统(IDS)是网络管理系统中检测某些类型的网络攻击的关键部分。IDS被分类为分类机器,因此很可能会参与机器学习方案。许多研究表明,即使无法将最佳机器学习方案应用于IDS,也是如此。为了解决这个问题,由于深度学习计划在多个领域取得的成就,可以将其视为解决方案。因此,在这项研究中,我们提出了一个深度学习模型,该模型基于卷积神经网络(CNN)层并使用称为CNN-LSTM的长短期记忆(LSTM)层来对每个单个交通网络进行分类。我们使用NSL-KDD数据集作为基准,因此我们可以将我们提出的方法与其他现有工作的性能进行比较。K D D T e s t +而第二个是K D D T e s t -21,这更难以分类。结果表明,我们提出的方法优于其他现有工作。

更新日期:2020-07-21
down
wechat
bug