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Kurnia Adi Cahyanto
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Pengujian Rule-Based pada Dataset Log Server Menggunakan Support Vector Machine Berbasis Linear Discriminat Analysis untuk Deteksi Malicious Activity Cahyanto, Kurnia Adi; Al Hilmi, Muhammad Anis; Mustamiin, Muhamad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 2: April 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022924107

Abstract

Web server bertugas menjalankan aplikasi web untuk melayani request dari klien. Setiap interaksi yang dilakukan klien terhadap aplikasi web, tercatat pada catatan log server. Dari log tersebut, terdapat data detail tentang alamat IP, perangkat dan sumber klien, request pengguna, respon server, dan keterangan lainnya. Dari informasi pada log, dapat dipakai untuk keperluan pengamanan sistem, salah satunya dengan cara melakukan analisis menggunakan data mining terhadap aktifitas klien yang tercatat pada log server. Selain itu, jika terdapat file yang diunggah pengguna, dapat juga dikaitkan dalam analisis log server dalam mengenali pola aktifitas dan malicious file. Dataset log yang telah didapat, diolah dengan menggunakan pelabelan rule-based yang nantinya diuji dengan pemodelan Support Vector Machine berbasis Linear Discriminant Analysis. Proses mengklasifikasikan data log server dapat dilakukan untuk mengenali aktifitas yang termasuk serangan, usaha paksa untuk masuk sistem terhadap server atau bukan. Dari pemodelan yang dilakukan, didapat hasil bahwa algoritma SVM berbasis LDA memiliki tingkat akurasi training 90,2% dan testing 89,9% dalam melakukan pengujian rule-based untuk pelabelan aktifitas pada web server. AbstractThe web server is in charge of running web applications to serve requests from clients. Every interaction the client makes to the web application is logged in server logs. From these logs, there are detailed data about IP addresses, client devices and sources, user requests, server responses, and other information. From the information in the logs, it can be used for system security purposes, one of which is by performing analysis using data mining of client activities recorded on the server log. In addition, if there is a file uploaded by a user, it can also be linked in server log analysis in recognizing activity patterns and malicious files. The log dataset that has been obtained is processed using rule-based labeling which will later be tested with a Linear Discriminant Analysis-based Support Vector Machine modeling. The process of classifying server log data can be done to identify activities that include attacks, forced attempts to enter the system against the server or not. From the modeling, the results show that the LDA-based SVM algorithm has a training accuracy rate of 90,2% and testing 89,9% in performing rule-based testing for activity labeling on the web server. 
Co-Authors A. Sumarudin Ade Diana P Adi Suheryadi Ahmad Lubis Ghozali Ayu Amaliyah Didi Abdillah Diky Anwar Diyanto Diyanto Esti Mulyani Fachrul Pralienka Bani Muhamad Fachrul Pralienka Bani Muhamad Hakim Asrori Himawan, Salamet Nur Iryanto Iryanto Kuresih, Kuresih M Lukman Sifa Muhamad, Fachrul Pralienka Bani Muhammad Anis Al Hilmi Mulyani, Esti Mustamiin, Muhamad Nazha, Amelia Pamungkas, Kiki Adi Rahmat Gernowo Raswa, Raswa Rendi Rendi Rendi, Rendi Sembada, Muhammad Rifqi Sitanggang, Filemon Suheryadi, Adi Syahid Reza Mutahari Taufiq Alamsyah, Lana Wahyu Adi Wibowo Widayanti Willy Permana Putra