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A Hybrid NIDS Model Using Artificial Neural Network and D-S Evidence

机译：基于人工神经网络和D-S证据的混合NIDS模型

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摘要

Artificial Neural Networks (ANNs), especially back-propagation (BP) neural network, can improve the performance of intrusion detection systems. However, for the current network intrusion detection methods, the detection precision, especially for low-frequent attacks, detection stability and training time are still needed to be enhanced. In this paper, a new model which based on optimized BP neural network and Dempster-Shafer theory to solve the above problems and help NIDS to achieve higher detection rate, less false positive rate and stronger stability. The general process of the authors' model is as follows: firstly dividing the main extracted feature into several different feature subsets. Then, based on different feature subsets, different ANN models are trained to build the detection engine. Finally, the D-S evidence theory is employed to integration these results, and obtain the final result. The effectiveness of this method is verified by experimental simulation utilizing KDD Cup1999 dataset.
机译：人工神经网络（ANN），尤其是反向传播（BP）神经网络，可以提高入侵检测系统的性能。但是，对于当前的网络入侵检测方法，尤其是针对低频攻击的检测精度，仍需要提高检测稳定性和训练时间。本文提出了一种基于优化BP神经网络和Dempster-Shafer理论的模型，可以解决上述问题，帮助NIDS获得更高的检测率，更少的假阳性率和更强的稳定性。作者模型的一般过程如下：首先将提取的主要特征划分为几个不同的特征子集。然后，基于不同的特征子集，训练不同的ANN模型以构建检测引擎。最后，采用D-S证据理论对这些结果进行积分，并获得最终结果。通过使用KDD Cup1999数据集的实验仿真验证了该方法的有效性。

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