Skripsi

DETEKSI SERANGAN DISTRIBUTED DENIAL OF SERVICE (DDOS) PADA SISTEM SMARTHOME MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

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Penilaian

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The Internet of Things (IoT) offers numerous benefits but also increases security risks, one of which is Distributed Denial of Service (DDoS) attacks. These attacks can cripple Smarthome systems by flooding the network with excessive data traffic. This research aims to detect DDoS attacks on Smarthome devices using the Convolutional Neural Network (CNN) method. The dataset used was obtained from COMNETS in PCAP file format, which was then extracted into CSV format using CICFlowMeter. The data was processed through several preprocessing stages, including label encoding, feature selection, normalization, reshaping, and data splitting. The CNN model was built using an architecture consisting of Conv1D, MaxPooling1D, Flatten, Dense, and Dropout layers. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The best results were obtained with an accuracy of 98.28%, precision of 98.23%, recall of 100%, and F1-score of 98.28%. These results indicate that the CNN method is highly effective in detecting DDoS attacks on IoT-based Smarthome systems in real time and with high accuracy.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
M2507003560	T170562	T1705622025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1705622025

Publisher

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

Collation

xv, 91 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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