Collaborative DDoS Detection in collaborative intrusion detection system for Heterogeneous Network Using Multi Deep Learning Model Ensemble Stacking - Dalam bentuk pengganti sidang - Artikel Jurnal
DINANTI ALDILA
Informasi Umum
Kode
25.04.5372
Klasifikasi
000 - General Works
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Network Detection System
Dilihat
94 kali
Informasi Lainnya
Abstraksi
The growing diversity and complexity of network infrastructures have made detecting Distributed Denial-of-Service (DDoS) attacks increasingly difficult, particularly within heterogeneous environments. Traditional detection methods often struggle to maintain high performance across varied network conditions. However, this study addresses the issue by proposing a Collaborative Intrusion Detection System (CIDS) that utilizes ensemble stacking of multiple deep learning models to improve generalizability and detection accuracy. The framework combines several deep neural networks as base learners, with a meta-learner that integrates their outputs for final prediction. Evaluations were conducted using three NetFlow-based datasets—NF-ToN-IoT, NF-BoT-IoT, and NF-CSE-CIC-IDS2018—each representing different network states. The proposed method achieved a peak accuracy of 84.7\%, demonstrating that the ensemble stacking approach significantly enhances DDoS detection capabilities in collaborative and heterogeneous network environments.
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | DINANTI ALDILA |
| Jenis | Perorangan |
| Penyunting | Parman Sukarno, Aulia Arif Wardana |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika (International Class) |
| Kota | Bandung |
| Tahun | 2025 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
