Sistem Deteksi Intrusi Cerdas: Studi Perbandingan Algoritma Pembelajaran Mesin Untuk Keamanan Siber
DOI:
https://doi.org/10.59188/jurnalsostech.v3i11.987Keywords:
Keamanan Siber, Algoritma Pembelajaran Mesin, Perbandingan Kinerja, Akurasi Deteksi Serangan, Tingkat Kebocoran, Respons SistemAbstract
Dalam era yang terus berkembang di bidang teknologi informasi, keamanan siber menjadi aspek kritis yang memerlukan perhatian mendalam. Studi ini membahas implementasi Sistem Deteksi Intrusi (IDS) yang cerdas dengan fokus pada perbandingan kinerja berbagai algoritma pembelajaran mesin yang diterapkan dalam lingkungan keamanan siber. Tujuan penelitian adalah untuk mengevaluasi akurasi deteksi serangan, tingkat kebocoran, dan respons sistem pada berbagai algoritma. Metode penelitian kuantitatif digunakan dengan merancang eksperimen pada sejumlah organisasi yang mewakili berbagai sektor industri. Hasil penelitian memberikan wawasan mendalam tentang keefektifan relatif algoritma pembelajaran mesin dalam meningkatkan keamanan siber, membimbing pemilihan dan implementasi IDS yang optimal.
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Isma Elan Maulani, Dwi Rayhan Sunandar Putra, Komarudin Komarudin
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA). that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
