• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Security and Networks >A hybrid PSO-SVM model for network intrusion detection
【24h】

A hybrid PSO-SVM model for network intrusion detection

机译：用于网络入侵检测的混合PSO-SVM模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper concentrates on the problem of network intrusion detection, which is an important problem in informatisation construction. We utilise the incremental support vector machine (SVM) to solve the network intrusion detection problem, and the SVM classification problem can be tackled by a decision function via a quadratic program. Particularly, the incremental SVM is used to train an SVM classifier with a partition of the given dataset; at the same time, support vectors at every step are reserved and the training set for the next iteration is constructed. Furthermore, the crucial problem of the incremental SVM is to impose the (Karush-Kuhn-Tucker) KKT conditions on the training dataset when adding a new vector. Moreover, to optimise parameters in the incremental SVM, particle swarm optimisation is utilised. If there is at least one sample in the set incremental training sample dataset, which cannot satisfy the KKT condition, the SVM classifiers to detect network intrusion can be obtained. To make performance evaluation of the proposed algorithm, experiments are conducted using the 'KDD Cup 1999' dataset. Experimental results demonstrate that compared with other corresponding methods, the proposed algorithm can effectively detect network intrusion behaviours with high accuracy rate and low time consumption.
机译：本文着重研究网络入侵检测问题，这是信息化建设中的重要问题。我们利用增量支持向量机（SVM）解决网络入侵检测问题，并且可以通过决策函数通过二次程序来解决SVM分类问题。特别地，增量式SVM用于训练具有给定数据集分区的SVM分类器。同时，保留每个步骤的支持向量，并为下一次迭代构建训练集。此外，增量SVM的关键问题是在添加新向量时将（Karush-Kuhn-Tucker）KKT条件强加给训练数据集。此外，为了优化增量SVM中的参数，使用了粒子群优化方法。如果设置的增量训练样本数据集中至少有一个样本不能满足KKT条件，则可以得到SVM分类器来检测网络入侵。为了对提出的算法进行性能评估，使用“ KDD Cup 1999”数据集进行了实验。实验结果表明，与其他相应方法相比，该算法能够以较高的准确率和较低的时间开销有效地检测网络入侵行为。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号