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首页> 外文会议>International Conference on Cloud Computing and Security >On-Line Intrusion Detection Model Based on Approximate Linear Dependent Condition with Linear Latent Feature Extraction
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On-Line Intrusion Detection Model Based on Approximate Linear Dependent Condition with Linear Latent Feature Extraction

机译：基于近似线性相关条件和线性潜在特征提取的在线入侵检测模型

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摘要

Most of the intrusion detection models (IDM) are constructed with off-line training data. Time-variance characteristic of the practical network system cannot be embodied in the off-line constructed IDM. On-line updating of the off-line IDM with the valued new samples is very necessary. In this paper, a new on-line instruction detection model based on approximate linear dependent (ALD) condition with linear latent feature extraction is proposed to address this problem. Specifically, the valued samples which can represent drift of the practical network are indentified with ALD and prior knowledge. Then, these selected samples are used to update the off-line IDM based on on-line latent feature extraction method and fast machine learning algorithm with sample-based updating strategy. Experiments based on KDD99 data are used to validate the proposed approach.
机译：大多数入侵检测模型（IDM）都是使用离线训练数据构建的。实际网络系统的时变特性无法体现在离线构建的IDM中。使用有价值的新样本对离线IDM进行在线更新非常必要。为了解决这一问题，本文提出了一种基于近似线性依赖（ALD）条件和线性潜在特征提取的在线指令检测模型。具体来说，可以用ALD和先验知识确定可以代表实际网络漂移的有价值样本。然后，这些选择的样本用于基于在线潜在特征提取方法和具有基于样本的更新策略的快速机器学习算法来更新离线IDM。基于KDD99数据的实验用于验证所提出的方法。

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