Deep Learning-based Feature Selection for Intrusion Detection System in Transport Layer 1 )
Deep Learning-based Feature Selection for Intrusion Detection System in Transport Layer 1 )
复制标题
传输层入侵检测系统中基于深度学习的特征选择（1）
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Kwangjo Kim
中科院分区：
文献类型：
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作者：
M. E. Aminanto;Kwangjo Kim
许多机器学习算法应用于入侵检测系统（IDS）来检测巨大的攻击。然而，由于输入特征庞大且复杂，机器很难全局学习攻击属性。特征选择可以通过选择最重要的特征来减少输入特征的维度来克服这个问题。我们利用人工神经网络（ANN）进行特征选择。另外，为了适用于资源受限的设备，我们可以基于TCP/IP层将IDS分成更小的部分，因为不同层有特定的攻击类型。我们展示传输层的 IDS 仅作为概念证明。我们应用属于深度学习算法的堆叠自动编码器（SAE）作为KDD99数据集的分类器。我们的实验表明，减少的输入特征足以完成分类任务。 한국정보보호학회 하계학술대회 논문집 Vol. 1 26、1号
Numerous machine learning algorithms applied on Intrusion Detection System (IDS) to detect enormous attacks. However, it is difficult for machine to learn attack properties globally since there are huge and complex input features. Feature selection can overcome this problem by selecting the most important features only to reduce the dimensionality of input features. We leverage Artificial Neural Network (ANN) for the feature selection. In addition, in order to be suitable for resource-constrained devices, we can divide the IDS into smaller parts based on TCP/IP layer since different layer has specific attack types. We show the IDS for transport layer only as a prove of concept. We apply Stacked Auto Encoder (SAE) which belongs to deep learning algorithm as a classifier for KDD99 Dataset. Our experiment shows that the reduced input features are sufficient for classification task. 한국정보보호학회 하계학술대회 논문집 Vol. 26, No. 1