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IMPLEMENTASI ALGORITMA RANDOM FOREST DAN SELEKSI FITUR CHI-SQUARE PADA KLASIFIKASI SERANGAN BOTNET DI JARINGAN INTERNET OF THINGS (IoT)

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Penilaian

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Along with the development of the term Internet of Things (IoT) in the present day, more and more hardware or electronics are connected to the internet. This allows many devices to be potentially affected by botnet attacks. Botnets are one of the most common threats to systems and the security of IoT devices or networks in the era of cloud-based computing in modern times. Therefore, it is very important to understand the anatomy of botnets, classify botnet attacks, and what mechanisms can be used to deal with botnet-based attacks that occur on IoT devices and networks. Machine Learning (ML) has been used in research as one of the potential solutions in facing the threat of botnet attacks on IoT. Machine Learning also requires feature selection that can help reduce the number of features present in the dataset and choose the most suitable features for classification. This research uses Network Dataset on ToN-IoT Dataset developed by cyber Range Laboratory at University of New South Wales, Canberra. Chi-Square feature selection is applied to select the best features, and Random Forest algorithm is used in the classification process. The results of the classification using Chi-Square feature selection were able to obtain the level of accuracy, precision, recall, specificity, F1-Score, and error values well and with a relatively low classification error rate compared to classification results without feature selection, where the best results have an accuracy value of 99.83%, precision of 99.94%, Recall of 99.57%, specificity of 99.97%, F1-Score of 99.75%, and a low error value of 0.17%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207003306	T77406	T774062022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T774062022

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer., 2022

Collation

xIi, 55 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.507

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

penyimpanan
Jurusan Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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