Skripsi

PERBANDINGAN METODE SELEKSI FITUR PADA SISTEM KLASIFIKASI BOTNET IoT MENGGUNKAN ALGORITMA RANDOM FOREST

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Penilaian

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Botnet attacks are one of the most serious threats of many threats in the rapid development of Internet of Things (IoT) devices. The more complex IoT devices make the detection or classifying time of attacks longer and consume a lot of memory. This study used MedBIoT datasets from Tallinn University Of Technology. Extra trees feature selection method and correlation feature selection are applied to select the best features. In addition, the random forest algorithm is also applied to the classification process. Classification results using selected features are able to obtain excellent levels of accuracy, sensitivity, specificity, precision, and F1 scores with faster processing times and with relatively low levels of misclassification.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002634	T51127	T511272021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T511272021

Publisher

Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Uniersitas Sriwijaya., 2021

Collation

xiv, 110 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.707

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Komputer
Data Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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