具有模糊聚类神经网络的贝叶斯信念网络/系统
许多研究认为人工神经网络 (ANN) 可以 提高入侵检测系统（IDS）的性能 与传统方法相比。然而对于基于 ANN 的 IDS， 检测精度，特别是对于低频攻击，以及 检测稳定性仍有待提高。一种新方法是 称为FC-ANN，基于ANN和模糊聚类，来解决该问题 帮助IDS实现更高的检测率、更低的误报率 以及更强的稳定性。 FC-ANN的一般流程如下： 首先利用模糊聚类技术生成不同的 训练子集。随后，基于不同的训练子集， 不同的 ANN 模型经过训练以制定不同的基础模型。 最后，采用元学习器、模糊聚合模块来 汇总这些结果。 KDD CUP 1999 实验结果 数据集显示所提出的新方法 FC-ANN 优于 BPNN 以及其他众所周知的方法，例如决策树、朴素贝叶斯 检测精度和检测稳定性方面。
问题：
是否可以将贝叶斯置信网络/系统与模糊聚类神经网络结合起来进行入侵检测？
谁能预见我可能遇到的任何问题？您的意见将是最有价值的。
Many researches have argued that Artificial Neural Networks (ANNs) can
improve the performance of intrusion detection systems (IDS) when
compared with traditional methods. However for ANN-based IDS,
detection precision, especially for low-frequent attacks, and
detection stability are still needed to be enhanced. A new approach is
called FC-ANN, based on ANN and fuzzy clustering, to solve the problem
and help IDS achieve higher detection rate, less false positive rate
and stronger stability. The general procedure of FC-ANN is as follows:
firstly fuzzy clustering technique is used to generate different
training subsets. Subsequently, based on different training subsets,
different ANN models are trained to formulate different base models.
Finally, a meta-learner, fuzzy aggregation module, is employed to
aggregate these results. Experimental results on the KDD CUP 1999
dataset show that the proposed new approach, FC-ANN, outperforms BPNN
and other well-known methods such as decision tree, the naïve Bayes in
terms of detection precision and detection stability.
Question:
Would it be possible to combine a Bayesian belief network/system with Fuzzy Clustering neural networks for intrusion detection?
Can anyone foresee any problems I may encounter? Your input would be most valuable.
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