Performance Evaluation of Double Layer Machine Learning Approach With Boosting Algorithm for Network Intrusion Detection System on Internet of Things - Dalam bentuk buku karya ilmiah
MUHAMMAD HAFIZ AMRULLAH
Informasi Umum
Kode
24.05.452
Klasifikasi
006.31 - Machine Learning
Jenis
Karya Ilmiah - Thesis (S2) - Reference
Subjek
Machine Learning
Dilihat
107 kali
Informasi Lainnya
Abstraksi
At this time, the development of Internet of Things (IoT) application networks is growing very rapidly. This growth leads many hackers to carry out more active attacks on IoT networks. e.g. DDoS, DoS, Mirai, recon, etc attacks that can attack the confidentiality, integrity, and availability of the IoT network. therefore a security system technology is needed to be implemented on the IoT networks.<br /> <br /> Intrusion Detection System (IDS) is an example of technology that can prevent these attacks. In order to detect anomalies and invisible attacks accurately, machine learning classifiers lead to more robust performance in IDS. There is a lot of normal data in network traffic, but abnormal data only accounts for a small part of the network traffic. However, some single machine learning classifier algorithms with class imbalance problems generally have low performance and accuracy in detecting detailed attack types.<br /> <br /> Considering the class imbalance in large-scale network flow data, this research
- TTI6D3 - INTERNET OF THINGS
- TTI6H3 - KEAMANAN SIBER LANJUT
- TEI6A3 - SISTEM CERDAS
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
Anda harus log in untuk mengakses flippingbook
Pengarang
| Nama | MUHAMMAD HAFIZ AMRULLAH |
| Jenis | Perorangan |
| Penyunting | Favian Dewanta |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S2 Teknik Elektro |
| Kota | Bandung |
| Tahun | 2024 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
