Incorporating a Honeyfarm with NNMLFF IDS for Improving Intrusion Detection

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loye ray


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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Author Biography

loye ray, Colorado Technical University

Dr Loye Ray is an adjunct professor at the University of Maryland University College and Colorado Technical University. He teaches and develops graduate and doctorate level cybersecurity courses. Loye has been teaching for nearly 30 years. He has held senior positions in industry, federal service and academics. His research areas include software security, network security and neural networks. He holds degrees in electrical engineering, business administration and computer science.


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