KLASIFIKASI TINGKAT KEPARAHAN SERANGAN JARINGAN KOMPUTER DENGAN METODE MACHINE LEARNING

  • Okki Setyawan Universitas Nusa Mandiri
  • Angge Firizkiansah Universitas Nusa Mandiri
  • Ahmad Nuryanto Universitas Nusa Mandiri

Abstract

Computer networks are currently developing very rapidly, so that many electronic devices are connected to the internet, but the security system adopted by these devices must be qualified so they are not vulnerable to threats and dangers. Researchers want to find out how severe the threat of an attack is detected by a firewall using data records from a company, using machine learning, namely K-Nearest Neighbors, Decission Tree. Classification of the severity of a computer network security system is usually called the severity level. In this study, the limitation of the seriousness level of the attack was divided into 3 parts from the highest level, namely critical, high and medium. The processed dataset is logging into the firewall as many as 5999 with 23 columns or features. The best of the three methods are K-Nearest Neighbors getting 100% accuracy and Decission Tree getting 100% accuracy  . With the results of this data processing, the machine learning method is very suitable to be used to classify the severity of computer network attacks

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Author Biographies

Okki Setyawan, Universitas Nusa Mandiri

Program Studi Magister Ilmu Komputer (S2), Fakultas Teknologi Informasi

Angge Firizkiansah, Universitas Nusa Mandiri

Program Studi Magister Ilmu Komputer (S2), Fakultas Teknologi Informasi

Ahmad Nuryanto, Universitas Nusa Mandiri

Program Studi Magister Ilmu Komputer (S2), Fakultas Teknologi Informasi

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Published
2021-06-20
How to Cite
SETYAWAN, Okki; FIRIZKIANSAH, Angge; NURYANTO, Ahmad. KLASIFIKASI TINGKAT KEPARAHAN SERANGAN JARINGAN KOMPUTER DENGAN METODE MACHINE LEARNING. Journal of Information System, Informatics and Computing, [S.l.], v. 5, n. 1, p. 128-133, june 2021. ISSN 2597-3673. Available at: <https://journal.stmikjayakarta.ac.id/index.php/jisicom/article/view/443>. Date accessed: 28 apr. 2025. doi: https://doi.org/10.52362/jisicom.v5i1.443.