Skripsi

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

---

Penilaian

---

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.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003564	T170563	T1705632025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1705632025

Publisher

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

Collation

xv, 87 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

MI

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment