Abstract
Today’s networks must deal with dynamically changing threats each day. Use of static datasets to train and prepare multi-layer feed forward neural network intrusion detection systems (MLFFNN IDS) doesn’t address these new threats. The use of real traffic data to train neural network IDSs has been out of reach in organizations due to privacy and concerns. Now the use of a honeyfarm system can provide real-time data to a MLFFNN IDS so that it can adjust to new threats as they begin. This system also removes the privacy and concerns since information about the network is false and acts as a decoy to lure attackers away from the real organizational network. This paper introduces a honeyfarm architecture one can use with a MLFFNN IDS to improve intrusion detection capability.
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