SISTEM DETEKSI SERANGAN DISTRIBUTED DENIAL OF SERVICE (DDOS) PADA PERANGKAT SMARTHOME MENGGUNAKAN METODE RECCURENT NEURAL NETWORK (RNN)

OVILIA, HEPRA and Stiawan, Deris and Afifah, Nurul (2025) SISTEM DETEKSI SERANGAN DISTRIBUTED DENIAL OF SERVICE (DDOS) PADA PERANGKAT SMARTHOME MENGGUNAKAN METODE RECCURENT NEURAL NETWORK (RNN). Undergraduate thesis, Sriwijaya University.

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Abstract

The Internet of Things (IoT) has facilitated the automation of household device management through the implementation of smart home concepts. However, the increasing number of connected devices in a network also raises the risk of cyberattacks, one of which is the Distributed Denial of Service (DDoS) attack that can disrupt service availability. This study aims to detect DDoS attacks on smart home devices using the Recurrent Neural Network (RNN) method, which is known for its effectiveness in handling sequential data. The dataset used originates from the COMNETS Smart Home team in .pcap format and is then extracted into .csv format using CICFlowMeter. The modeling stages include data understanding, feature selection, label encoding, normalization, data balancing using SMOTE, and data splitting for training and testing the model. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The test results show that the RNN model is capable of detecting DDoS attacks with an accuracy of 99.76%, precision of 99.53%, recall of 100%, and an F1-score of 99.76% on an 80:10:10 data split scenario. Therefore, the RNN model has proven effective in identifying DDoS attacks on smart home devices. Keywords: Internet of Things, Smarthome, DDoS, SNORT, RNN, Deep Learning, Attack Detection

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Internet of Things, Smarthome, DDoS, SNORT, RNN, Deep Learning, Attack Detection
Subjects:	T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.85 Network systems theory Including network analysis Cf. TS157.5+ Scheduling
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Hepra Ovilia
Date Deposited:	30 Jun 2025 02:10
Last Modified:	30 Jun 2025 02:10
URI:	http://repository.unsri.ac.id/id/eprint/176091

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