ANALISIS EFEKTIVITAS MULTILAYER PERCEPTRON (MLP) DALAM MENDETEKSI SERANGAN DISTRIBUTED DENIAL OF SERVICE (DDOS) PADA EKOSISTEM SMART HOME BERBASIS IOT

RIZQULLAH, MUHAMMAD RAFI and Stiawan, Deris and Afifah, Nurul (2025) ANALISIS EFEKTIVITAS MULTILAYER PERCEPTRON (MLP) DALAM MENDETEKSI SERANGAN DISTRIBUTED DENIAL OF SERVICE (DDOS) PADA EKOSISTEM SMART HOME BERBASIS IOT. Undergraduate thesis, Sriwijaya University.

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Abstract

The Internet of Things (IoT) has brought various conveniences to everyday life, particularly in smart home applications. However, this increased connectivity also heightens the risk of cyber attacks, especially Distributed Denial of Service (DDoS) attacks. This study aims to analyze the effectiveness of the Multilayer Perceptron (MLP) method in detecting DDoS attacks within IoT-based smart home ecosystems. The dataset used was provided by the COMNETS team in pcap format and extracted into csv format using CICFlowMeter. The preprocessing stage involved label encoding, feature selection using a correlation matrix, data normalization with a min-max scaler, and data splitting with various ratios. The MLP model was designed with two hidden layers using ReLU activation functions, while the output layer employed a sigmoid activation function for binary classification. The model evaluation was conducted using a confusion matrix and evaluation metrics such as accuracy, precision, recall, and F1-score. Based on the test results, the MLP model achieved an optimal accuracy of 99.2% in the 60:20:20 data split scenario. This study demonstrates that MLP is effective in detecting DDoS attacks on IoT smart home devices and can serve as a potential solution for network security systems.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	DDoS, IoT, Smart Home, Multilayer Perceptron, Deep Learning
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:	Muhammad Rafi Rizqullah
Date Deposited:	30 Jun 2025 01:35
Last Modified:	30 Jun 2025 01:35
URI:	http://repository.unsri.ac.id/id/eprint/170563

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