Application Of Machine Learning Directed To Detect And Prevent Network Intrusion In Xyz Switching Company (Financial Switching Company)
DOI:
https://doi.org/10.31004/jpdk.v4i5.7597Abstract
Makalah ini menjelaskan perbandingan beberapa model pembelajaran mesin yang akan digunakan untuk mendeteksi dan mencegah intrusi jaringan, berdasarkan data yang dikumpulkan dari PT. Perangkat Firewall Generasi Berikutnya dari XYZ. Lalu lintas yang diterima ke lingkungan perusahaan dibagi menjadi tiga jenis yang berbeda yaitu diterima, dicegah dan ditolak. Algoritma yang dibandingkan adalah Decision Trees, Random Forest, Gradient Boosted Trees dan Naïve Bayes.Downloads
Published
2022-10-17
How to Cite
Christian, A. ., & Jayadi, R. . (2022). Application Of Machine Learning Directed To Detect And Prevent Network Intrusion In Xyz Switching Company (Financial Switching Company). Jurnal Pendidikan Dan Konseling (JPDK), 4(5), 5760–5772. https://doi.org/10.31004/jpdk.v4i5.7597
Issue
Section
Articles
License
Copyright (c) 2022 Alvin Christian, Riyanto Jayadi
![Creative Commons License](http://i.creativecommons.org/l/by-sa/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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 License that allows others to share the work with an acknowledgement of the works authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journals 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 (See The Effect of Open Access).