HiNFRA:层次神经模糊学习在线风险评估

K. Haslum, A. Abraham, S. J. Knapskog
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引用次数: 13

摘要

我们之前的研究说明了基于模糊逻辑的分布式入侵预测和防御系统(DIPPS)在线风险评估的设计[3]。在DIPPS传感器的基础上,我们提出了一种基于模糊逻辑的在线风险评估方案,而不是仅仅阻止攻击者或阻塞流量。本文提出了一种层次神经模糊在线风险评估(HiNFRA)模型,以辅助决策过程。利用神经网络学习技术实现了基于模糊逻辑的风险评估模型的微调。初步结果表明,神经学习技术可以提高模糊控制器的性能,使风险评估模型具有更强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HiNFRA: Hierarchical Neuro-Fuzzy Learning for Online Risk Assessment
Our previous research illustrated the design of fuzzy logic based online risk assessment for Distributed Intrusion Prediction and Prevention Systems (DIPPS) [3]. Based on the DIPPS sensors, instead of merely preventing the attackers or blocking traffic, we propose a fuzzy logic based online risk assessment scheme. This paper propose a Hierarchical Neuro-Fuzzy online Risk Assessment (HiNFRA) model to aid the decision making process of a DIPPS. The fine tuning of fuzzy logic based risk assessment model is achieved using a neural network learning technique. Preliminary results indicate that the neural learning technique could improve the fuzzy controller performance and make the risk assessment model more robust.
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