This paper reports one snapshot of our on-going experiments in which
a common target we call {\it a-tiny-island-in-a-huge-lake} is explored
 with different methods ranging from a data-mining technique
to an artificial immune system. 
Our implicit interest is a network intrusion
detection where we usually do not know what does an illegal transaction pattern
look like until it completed intrusion when it was too late.
Hence our first interest is (i) if it is possible
to train the intrusion detection system {\it only using legal patterns.} 
From this context we assume data floating in the {\it lake} 
are normal while ones found on the {island} is abnormal. 
Our second concern is then
(ii) to study the limit of the size of the detectable area,
that is,  when we decrease the size of the island shrinking to zero
until which size can the detector detect it.
In this paper
a fuzzy rule extraction implemented by a neural network architecture is employed.
for the purpose, 

