Skripsi

DETEKSI SERANGAN DDOS PADA SISTEM SMARTHOME DENGAN METODE DEEP LEARNING

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Penilaian

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The advancement of the Internet of Things (IoT) enables physical devices such as cameras, home doors, televisions, lights, and other household appliances to connect to the internet, forming an intelligent and convenient Smart Home system. However, the connectivity among these heterogeneous devices also increases vulnerability to cyberattacks, particularly Distributed Denial of Service (DDoS) attacks. In this study, Deep Learning methods, specifically Deep Neural Networks (DNN) and Autoencoders (AE), are employed to detect DDoS attacks within the dataset. Tools such as the Snort Intrusion Detection System (IDS) are utilized to identify DDoS attacks, while CICFlowMeter is used to extract data from pcap format into csv format. The results of this study demonstrate that the Autoencoder method effectively performs feature extraction and dimensionality reduction, achieving optimal performance using an 80% training and 20% testing split, with a training loss of 0.0052 and validation loss of 0.0054. The features are then classified using a Deep Neural Network with 250 epochs, yielding evaluation metrics of 99.53% accuracy, 99.53% precision, 99.53% recall, and 99.53% F1- score.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003562	T176055	T1760552025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1760552025

Publisher

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

Collation

xvi, 88 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

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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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