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AN IMPROVED NETWORK INTRUSION DETECTION METHOD BASED ON VQ-SVM

机译:一种基于VQ-SVM的改进的网络入侵检测方法

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This paper proposes an improved efficient algorithm based on Vector Quantization and Support Vector Machine (VQ-SVM) for intrusion detection. The algorithm firstly reduces the network auditing dataset by using Vector Quantization technique, produces a training codebook, and then adopts fast training algorithm for Support Vector Machine to build intrusion detection model on the codebook. The experiment results indicate that the detecting efficiency of the intrusion detection model based on VQ-SVM algorithm can be greatly improved in comparison with the traditional SVM method, whereas the detecting accuracy doesn't decline somewhat.
机译:提出了一种基于矢量量化和支持向量机(VQ-SVM)的改进的高效入侵检测算法。该算法首先利用向量量化技术减少网络审计数据集,生成训练码本,然后对支持向量机采用快速训练算法,在码本上建立入侵检测模型。实验结果表明,与传统的支持向量机方法相比,基于VQ-SVM算法的入侵检测模型的检测效率有较大提高,但检测精度并未有所下降。

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