Title :
Network Intrusion Detection System Using Neural Networks
Author :
Shun, Julian ; Malki, Heidar A.
Author_Institution :
Coll. of Technol., Univ. of Houston, Houston, TX
Abstract :
This paper presents a neural network-based intrusion detection method for the internet-based attacks on a computer network. Intrusion detection systems (IDS) have been created to predict and thwart current and future attacks. Neural networks are used to identify and predict unusual activities in the system. In particular, feedforward neural networks with the back propagation training algorithm were employed in this study. Training and testing data were obtained from the Defense Advanced Research Projects Agency (DARPA) intrusion detection evaluation data sets. The experimental results on real-data showed promising results on detection intrusion systems using neural networks.
Keywords :
Internet; backpropagation; feedforward neural nets; security of data; telecommunication security; Defense Advanced Research Projects Agency; Internet-based attacks; back propagation training algorithm; computer network; feedforward neural networks; network intrusion detection system; Computer hacking; Computer networks; Computer security; Data security; Information security; Internet; Intrusion detection; National security; Neural networks; Telecommunication traffic; Intrusion detection; Neural networks;
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
DOI :
10.1109/ICNC.2008.900