Tytuł artykułu

Ensemble learning approach to enhancing binary classification in Intrusion Detection System for Internet of Things

Tytuł czasopisma

International Journal of Electronics and Telecommunications

Rocznik

2024

Wolumin

vol. 70

Numer

No 2

Afiliacje

Soni : Faculty of Computer Sciences, Universitas Muhammadiyah Riau, Pekanbaru, Riau Indonesia and Faculty of Data Science and Computing, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia ; Remli, Muhammad Akmal : Faculty of Data Science and Computing, Universiti Malaysia Kelantan and Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia ; Mohd Daud, Kauthar : Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi Selangor, Malaysia ; Amien, Januar Al : Universitas Muhammadiyah Riau, Pekanbaru, Riau Indonesia and Faculty of Data Science and Computing, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia

Autorzy

Soni ; Remli, Muhammad Akmal ; Mohd Daud, Kauthar ; Amien, Januar Al

Słowa kluczowe

Binary classification ; XGBoost ; ToN IoT dataset ; ensemble technique

Wydział PAN

Nauki Techniczne

Zakres

465-472

Wydawca

Polish Academy of Sciences Committee of Electronics and Telecommunications

Data

25.06.2024

Typ

Article

Identyfikator

DOI: 10.24425/ijet.2024.149567 ; ISSN 2081-8491, eISSN 2300-1933