DocumentCode
3245752
Title
An application of a recurrent network to an intrusion detection system
Author
Debar, Hervé ; Dorizzi, Bernadette
Author_Institution
CSEE/DCI, Les Ulis, France
Volume
2
fYear
1992
fDate
7-11 Jun 1992
Firstpage
478
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location
Baltimore, MD
Print_ISBN
0-7803-0559-0
Type
conf
DOI
10.1109/IJCNN.1992.226942
Filename
226942
Link To Document