The invention provides a method for detecting intrusion by multiple neuronic network
confusion, which comprises the following steps: data from external network is subject to packet sniffing and analyzing; the processed
network data is simultaneously transmitted to an analytical
database and a SGNG exception detection classifier, and the SGNG exception detection classifier is trained offline by normal categorical data acquired by a close network; the SGNG exception detection classifier identifies the detected exceptional data, carries out
system alarm and stores the exceptional data into the analytical
database; a
data set which is identified as exception in the analytical
database is provided for a PCSOM exception cluster analyzer for exceptional
data cluster analysis; the exceptional data detected by the SGNG exception detection classifier is input to a plurality of parallel PCANN misuse
detector respectively according to classifications; the PCANN misuse
detector carries out concrete intrusion classification alarm on the detected exceptional data, simultaneously, all the exceptional data filtered by the PCANN misuse
detector is identified and stored in the analytical database.