Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Automatic detection of network intrusion is a challenging task because of increasing types of attacks. Many of the existing approaches either are rigid, inflexible designs tailored to a specific situation or require manual setting of design parameters such as the initial number of clusters.   allow the design parameters to be determined dynamically by adopting a layered hybrid architecture, hence resolving the aforementioned shortcomings. The first layer uses FCM and GK fuzzy clustering to extract the features and the second layer uses a set of RBF neural networks to perform the classification. The flexible design parameters are initial number of clusters, number of RBF networks and number of neurons inside each network which are determined with minimal input from the user. The simulation result shows high detection rates as ll as fer false positives compared to earlier approaches.
['Ahmad Ghadiri', 'Nasser Ghadiri']
I am writing a research paper on a idea An Adaptive Hybrid Architecture for Intrusion Detection Based on Fuzzy Clustering and RBF Neural Networks
[{"from": "human", "value": "I am writing a research paper on a idea An Adaptive Hybrid Architecture for Intrusion Detection Based on Fuzzy Clustering and RBF Neural Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Automatic detection of network intrusion is a challenging task because of increasing types of attacks. Many of the existing approaches either are rigid, inflexible designs tailored to a specific situation or require manual setting of design parameters such as the initial number of clusters.   allow the design parameters to be determined dynamically by adopting a layered hybrid architecture, hence resolving the aforementioned shortcomings. The first layer uses FCM and GK fuzzy clustering to extract the features and the second layer uses a set of RBF neural networks to perform the classification. The flexible design parameters are initial number of clusters, number of RBF networks and number of neurons inside each network which are determined with minimal input from the user. The simulation result shows high detection rates as ll as fer false positives compared to earlier approaches."}]