MEMPREDIKSI SERANGAN PADA SIM (SECURITY INFORMATION MANAGEMENT) DENGAN MENGGUNAKAN ALGORITMA HIDDEN MARKOV MODEL

Rico Andreas, . (2020) MEMPREDIKSI SERANGAN PADA SIM (SECURITY INFORMATION MANAGEMENT) DENGAN MENGGUNAKAN ALGORITMA HIDDEN MARKOV MODEL. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

Website is an app that is easily accessed anywhere and anytime, in the ease there is an attack by hackers that can be carried out on a website that is directed directly to the web server. These attacks have threats that have been recorded by OWASP (Open Web Application Security Project) in 2017 thus creating information on OWASP Security 10 - 2017 specifically on web applications. With these threats, this research was conducted to create a system that can detect an attack that occurs on a website, especially on a web server and can monitor and display information on existing activities on a web server with a client. Security Information Management (SIM) will read access log and error log data that have been recorded by the web server and then the data will be conducted training and testing using the Hidden Markov Model algorithm so that it gets a learning model for the system to detect an attack, as well as access logs and error log will be translated into information that is easily read by sysadmin into a dashboard. This research is expected to produce a model that can detect and monitor web server activities in an attack.

Item Type: Thesis (Skripsi)
Additional Information: [No. Panggil: 1610511080] [Dosen Pembimbing 1: Henki Bayu Seta] [Dosen Pembimbing 2: Nurul Chamidah] [Dosen Penguji 1: Yuni Widiastiwi] [Dosen Penguji 2: Mayanda Mega Santoni]
Uncontrolled Keywords: Access log, Error log, Security Information Management, SIM, Hidden Markov Model
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: Rico Andreas
Date Deposited: 13 Jan 2022 02:27
Last Modified: 13 Jan 2022 02:27
URI: http://repository.upnvj.ac.id/id/eprint/7430

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