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DETEKSI SERANGAN DDOS PADA SMARTHOME MENGGUNAKAN METODE RANDOM FOREST

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The development of Internet of Things (IoT) technology has led to the widespread adoption of smart home devices. However, their connection to the internet also makes them vulnerable to cyberattacks, particularly Distributed Denial of Service (DDoS) attacks. This study aims to implement and evaluate the Random Forest algorithm in detecting DDoS attacks on smart home network environments. The dataset used in this research is COMNETSSMARTHOME, which includes both normal and malicious traffic data, specifically SYN Flood attacks. The research process involves data collection, preprocessing (data cleaning, encoding, and normalization), model training using Random Forest, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate that the Random Forest model can detect DDoS attacks with 100% accuracy and no classification errors. Furthermore, features such as Fwd Packet Length Std, Flow Bytes/s, and SYN Flag Count were identified as the most influential in the classification process. This research concludes that Random Forest is an effective method for detecting DDoS attacks on smart home devices and can serve as a foundation for developing automated security systems based on machine learning.

Availability
Inventory Code Barcode Call Number Location Status
2507005496T183138T1831382025Central Library (Referensi)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1831382025
Publisher
Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xxii, 549 hlm.; ilus.; tab.; 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
004.07
Content Type
Text
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Sistem komputer
Specific Detail Info
-
Statement of Responsibility
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No other version available

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