Towards real-time intrusion detection using fuzzy cognitive maps modeling and simulation
Towards real-time intrusion detection using fuzzy cognitive maps modeling and simulation
复制标题
使用模糊认知图建模和仿真实现实时入侵检测
DOI：
10.1109/itsim.2008.4631676
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
A. Jantan
中科院分区：
文献类型：
--
作者：
M. Jazzar;A. Jantan
模糊认知图是一种理想的因果知识获取工具，具有模糊符号图，可表示为联想单层神经网络。使用FCM，我们的方法试图根据网络流量数据与攻击或正常连接的相关性来诊断和定向网络流量数据。通过量化因果推理过程，我们可以确定攻击检测和奇数包的严重程度。这样，与攻击具有低因果关系的分组可以被丢弃或忽略，和/或与攻击具有高因果关系的分组将被突出显示。本文提出了一种利用FCM复制正常网络连接和攻击网络连接的实时入侵检测方法。
Fuzzy cognitive maps (FCM) are ideal causal knowledge acquiring tools with fuzzy signed graphs which can be presented as an associative single layer neural network. Using FCM, our methodology attempt to diagnose and direct network traffic data based on its relevance to attack or normal connections. By quantifying the causal inference process, we can determine the attack detection and the severity of odd packets. As such, packets with low causal relations to attacks can be dropped or ignored and/or packets with high causal relations to attacks are to be highlighted. In this paper, we present a new real-time intrusion detection approach using FCM to replicate normal and attack network connection.