Remote to local attack detection using supervised neural network

Ahmad, I. and Abdullah, A.B. and Alghamdi, A.S. (2010) Remote to local attack detection using supervised neural network. In: UNSPECIFIED.

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Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: 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
Uncontrolled Keywords: Artificial Neural Network; Attack detection; Data sets; False negative rate; False positive rates; Intrusion detection approaches; Local attack; Supervised neural networks, Feedforward neural networks; Internet, Intrusion detection
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:49
Last Modified: 09 Nov 2023 15:49
URI: https://khub.utp.edu.my/scholars/id/eprint/995

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