Intrusion Detection and Attack Classification Using Feed-Forward Neural Network

Fast Internet growth and increase in number of users make network security essential in recent decades. Lately one of the most hot research topics in network security is intrusion detection systems (IDSs) which try to keep security at the highest level. This paper addresses a IDS using a 2-layered f...

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Bibliographic Details
Published in2010 Second International Conference on Computer and Network Technology pp. 262 - 266
Main Authors Haddadi, Fariba, Khanchi, Sara, Shetabi, Mehran, Derhami, Vali
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2010
Subjects
Artificial Neural network
Artificial neural networks
Back propagation
Computer networks
DARPA
Data security
Feed-forward
Feedforward neural networks
Feedforward systems
Information security
Internet
Intrusion
Intrusion detection
IP networks
Neural networks
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ISBN9780769540429
0769540422
9781424469611
1424469619
DOI10.1109/ICCNT.2010.28

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Summary:Fast Internet growth and increase in number of users make network security essential in recent decades. Lately one of the most hot research topics in network security is intrusion detection systems (IDSs) which try to keep security at the highest level. This paper addresses a IDS using a 2-layered feed-forward neural network. In training phase, "early stopping" strategy is used to overcome the "over-fitting" problem in neural networks. The proposed system is evaluated by DARPA dataset. The connections selected from DARPA is preprocessed and feature range is converted into [-1, 1]. These modifications affect final detection results notably. Experimental results show that the system, with simplicity in comparison with similar cases, has suitable performance with high precision.
ISBN:9780769540429
0769540422
9781424469611
1424469619
DOI:10.1109/ICCNT.2010.28