JURNAL INTERNASIONAL

Journal of Theoritical and Apllied Information Technology

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Maximizing detection accuracy and miniziming the false alarm rate are two major chellenges in the design of an anomaly Itrusion Detection System (IDS). These challenges can be handled by designing an ensemble classifier for detecting all classed of attaks. This is becouse, single classifier tehnique failts to achieve acceptable false alarm rate and detection accuary for all alsseds of attacks. In ensemble classifier, the ouput several algorithms used as predictors for a particular problem are combined to improve the detectio accuary and minimize false alarm rate of the overal system. Therefore, this paper has proposen a new esemble clssifeir based on clustering method to address the intursion detection problem in the network. The clustering techniques combined in the proposed esemble classifier are KM-GSA, Km-PSO and fuzzy C-Means (FCM). Experimental results showed an improvement in the detection accuary for all classes of network traffic i.e., Normal, Probe, DoS, U2R and R2L. Hence, this validates the proposed ensemble classifier.

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Ketersediaan

JI03220014

004.0285 JATIT J JurnalInternasional

Perpus STMIK (JurnalInternasional)

Tersedia

Informasi Detail

Judul Seri

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No. Panggil

004.0285 JATIT J JurnalInternasional

Penerbit

USA : JATIT., 2014

Deskripsi Fisik

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Bahasa

English

ISBN/ISSN

1992 8645

Klasifikasi

004.0285

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

Volume 70 Nomor 3 Tahun 2014

Subjek

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Info Detail Spesifik

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Pernyataan Tanggungjawab

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Versi lain/terkait

Lampiran Berkas

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