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A comparative analysis of Feed-forward neural network #x00026; Recurrent Neural network to detect intrusion

机译:前馈神经网络与经常性神经网络检测入侵的比较分析

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As computer networks are grows exponentially security in computer system has become a foremost issue. Monitoring atypical activity can be one way to detect any violation that impedes computer systems security. Existing methods like statistical models [12] for intrusion detection not perform well whereas Neural network has been proved as an efficient method for intrusion detection [10]. In this paper Feed-forward and Recurrent Neural network is trained by Back propagation training algorithm and using normal data. Performances of these Neural Networks are compared against both normal data and intrusive data.
机译:随着计算机网络在计算机系统中的指数安全性已成为最重要的问题。监控非典型活动可以是检测阻碍计算机系统安全性的任何违规的一种方式。统计模型的现有方法[12]用于入侵检测不表现良好,而神经网络已被证明是用于入侵检测的有效方法[10]。在本文中,前馈和经常性神经网络被回到传播训练算法和使用正常数据训练。将这些神经网络的性能与正常数据和侵入性数据进行比较。

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