Title :
An application of a recurrent network to an intrusion detection system
Author :
Debar, Hervé ; Dorizzi, Bernadette
Author_Institution :
CSEE/DCI, Les Ulis, France
Abstract :
The authors present an application of recurrent neural networks for intrusion detection. A partially recurrent network has been chosen for this particular application. The neural network acts as a data filter that highlights anomalous or suspicious data according to previously learned patterns. It has proven adaptive, because the same results for several users have been obtained with varying activities. The network cosine function was tested, and a hetero-associative version of the network was used to analyze the flipflop problem
Keywords :
access control; recurrent neural nets; safety systems; security of data; Elman neural net; Gent neural net; data filter; flipflop problem; hetero-associative; intrusion detection system; network cosine function; recurrent neural networks; suspicious data; Access control; Application software; Computer hacking; Computer security; Cryptography; Intrusion detection; Neural networks; Operating systems; Prototypes; Recurrent neural networks;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.226942
