A New Model for Intrusion Detection based on Reduced Error Pruning Technique
Автор: Mradul Dhakar, Akhilesh Tiwari
Журнал: International Journal of Computer Network and Information Security(IJCNIS) @ijcnis
Статья в выпуске: 11 vol.5, 2013 года.
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The increasing counterfeit of the internet usage has raised concerns of the security agencies to work very hard in order to diminish the presence of the abnormal users from the web. The motive of these illicit users (called intruders) is to harm the system or the network either by gaining access to the system or prohibiting genuine users to access the resources. Hence in order to tackle the abnormalities Intrusion Detection System (IDS) with Data Mining has evolved as the most demanding approach. On the one end IDS aims to detect the intrusions by monitoring a given environment while on the other end Data Mining allows mining of these intrusions hidden among genuine users. In this regard, IDS with Data Mining has been through several revisions in consideration to meet the current requirements with efficient detection of intrusions. Also several models have been proposed for enhancing the system performance. In context to improved performance, the paper presents a new model for intrusion detection. This improved model, named as REP (Reduced Error Pruning) based Intrusion Detection Model results in higher accuracy along with the increased number of correctly classified instances.
Data mining, intrusion detection, REP, K2, KDDCup’99
Короткий адрес: https://sciup.org/15011246
IDR: 15011246
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