Skripsi

DETEKSI SERANGAN DDOS DAN MITM PADA SISTEM SMART HOME MENGGUNAKAN METODE LIGHTGBM

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This study aims to detect and classify Distributed Denial of Service (DDoS) and Man-in-the-Middle (MiTM) attacks in smart home networks using the Light Gradient Boosting Machine (LightGBM) algorithm. With the rapid growth of Internet of Things (IoT) devices, cybersecurity challenges have become crucial due to vulnerabilities in smart home devices. This research utilizes the COMNETS SMARTHOME dataset, extracted using the T-Shark tool to generate relevant network features. The research stages include data preprocessing through feature selection using the Mutual Information method, label encoding, and addressing data imbalance using the Random Oversampling (ROS) technique. Model evaluation is conducted using accuracy, precision, recall, and F1-score metrics. The experimental results show that the LightGBM method provides high and consistent performance. Optimal performance was achieved in a data split scenario of 80% training and 20% testing, with an average accuracy of 99.71%. Specifically, the model achieved a precision of 99.92% for DDoS attacks and a recall of 100% for MiTM attacks. These results prove that LightGBM is effective and efficient in building a reliable intrusion detection system to protect smart home environments from cyber threats

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Availability

Detail Information

Series Title

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Call Number

T1901922025

Publisher

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

Collation

xiii, 88 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005.840 7

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer
Virus Komputer--Malware

Specific Detail Info

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Statement of Responsibility

MI

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