Skripsi

SISTEM DETEKSI SERANGAN DISTRIBUTED DENIAL OF SERVICE (DDOS) PADA PERANGKAT SMARTHOME MENGGUNAKAN METODE RECCURENT NEURAL NETWORK (RNN)

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Penilaian

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The Internet of Things (IoT) has facilitated the automation of household device management through the implementation of smart home concepts. However, the increasing number of connected devices in a network also raises the risk of cyberattacks, one of which is the Distributed Denial of Service (DDoS) attack that can disrupt service availability. This study aims to detect DDoS attacks on smart home devices using the Recurrent Neural Network (RNN) method, which is known for its effectiveness in handling sequential data. The dataset used originates from the COMNETS Smart Home team in .pcap format and is then extracted into .csv format using CICFlowMeter. The modeling stages include data understanding, feature selection, label encoding, normalization, data balancing using SMOTE, and data splitting for training and testing the model. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The test results show that the RNN model is capable of detecting DDoS attacks with an accuracy of 99.76%, precision of 99.53%, recall of 100%, and an F1-score of 99.76% on an 80:10:10 data split scenario. Therefore, the RNN model has proven effective in identifying DDoS attacks on smart home devices. Keywords: Internet of Things, Smarthome, DDoS, SNORT, RNN, Deep Learning, Attack Detection

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003563	T176091	T1760912025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1760912025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 85 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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