Skripsi

DETEKSI SERANGAN DOS PADA JARINGAN SMART HOME IPV6 MENGGUNAKAN METODE LOGISTIC REGRESSION

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Denial of Service (DoS) attacks pose a serious threat to IPv6-based smart home networks, causing disruptions in device connectivity and significantly reducing system performance. This study aims to detect DoS attacks in IPv6 smart home networks using the Logistic Regression machine learning algorithm. The dataset was generated from network traffic captured using the THC-IPv6 tool, followed by feature extraction, labeling, encoding, and data balancing using the Random Oversampling (ROS) method to ensure equal class distribution. The model implementation was carried out in the Google Colab environment with a data split of 90% for training and 10% for testing. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics derived from the confusion matrix. The results show that the Logistic Regression method performs well in detecting DoS attacks, achieving 87.19% accuracy, 79.68% precision, 100% recall, and an 88.66% F1-score. These findings demonstrate that Logistic Regression can effectively detect DoS attacks in IPv6-based smart home networks, although some false positives remain. This research can serve as a foundation for developing real-time attack detection systems and applying other machine learning or deep learning algorithms to further improve detection accuracy in the future.

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Detail Information

Series Title

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Call Number

T1907252025

Publisher

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

Collation

x, 52 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.807

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

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