TY  - CONF
ID  - scholars995
A1  - Ahmad, I.
A1  - Abdullah, A.B.
A1  - Alghamdi, A.S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951491256&partnerID=40&md5=fe26cebbffbf045d14588da0c73a6f79
N1  - cited By 6; Conference of 2010 International Conference for Internet Technology and Secured Transactions, ICITST 2010 ; Conference Date: 8 November 2010 Through 11 November 2010; Conference Code:83777
CY  - London
N2  - In order to determine Remote to Local (R2L) attack, an intrusion detection technique based on artificial neural network is presented. This technique uses sampled dataset from Kddcup99 that is standard for benchmarking of attack detection tools. The back propagation algorithm is used for training the feedforward neural network. The developed system is applied to R2L attacks. Moreover, experiment indicates this technique has comparatively low false positive rate and false negative rate, consequently it effectively resolves the deficiency of existing intrusion detection approaches.
SN  - 9781424488629
KW  - Artificial Neural Network; Attack detection; Data sets; False negative rate; False positive rates; Intrusion detection approaches; Local attack; Supervised neural networks
KW  -  Feedforward neural networks; Internet
KW  -  Intrusion detection
TI  - Remote to local attack detection using supervised neural network
Y1  - 2010///
AV  - none
ER  -