Title :
Intrusion Recognition Using Neural Networks
Author :
Golovko, Vladimir ; Kochurko, Pavel
Author_Institution :
Brest State Tech. Univ., Brest
Abstract :
Intrusion detection techniques are of great importance for computer network protecting because of increasing the number of remote attack using TCP/IP protocols. There exist a number of intrusion detection systems, which are based on different approaches for anomalous behavior detection. This paper focuses on applying neural networks for attack recognition. It is based on multilayer perceptron. The 1999 KDD Cup data set is used for training and testing neural networks. The results of experiments are discussed in the paper.
Keywords :
computer networks; neural nets; security of data; transport protocols; TCP/IP protocols; intrusion detection systems; intrusion recognition; multilayer perceptron; neural networks; remote attack; Artificial neural networks; Computer crime; Computer networks; Intrusion detection; Multilayer perceptrons; Neural networks; Protection; Protocols; TCPIP; Telecommunication traffic; Neural networks; attack recognition; intrusion detection systems; network attacks;
Conference_Titel :
Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2005. IDAACS 2005. IEEE
Conference_Location :
Sofia
Print_ISBN :
0-7803-9445-3
Electronic_ISBN :
0-7803-9446-1
DOI :
10.1109/IDAACS.2005.282950
