ANALISIS KLASIFIKASI SHELLCODE DENGAN MACHINE LEARNING BERBASIS KLASIFIKASI BINER

SEMENDAWAI, JAKA NAUFAL and Stiawan, Deris and Saputra, Iwan Pahendra Anto (2024) ANALISIS KLASIFIKASI SHELLCODE DENGAN MACHINE LEARNING BERBASIS KLASIFIKASI BINER. Masters thesis, Sriwijaya University.

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Abstract

Internet dapat menghubungkan satu orang dengan orang lain dengan menggunakan perangkat masing-masing. Internet sendiri memiliki dampak positif dan negatif. Salah satu contoh dampak negatif dari internet adalah adanya malware yang dapat mengganggu atau bahkan membunuh perangkat atau penggunanya; itulah mengapa keamanan cyber diperlukan. Banyak metode yang dapat digunakan untuk mencegah atau mendeteksi malware. Salah satunya adalah dengan menggunakan teknik machine learning. Dataset pelatihan dan pengujian untuk eksperimen ini diambil dari dataset UNSW_NB15. K-Nearest Neighbour (KNN), Decision Tree, dan Naïve Bayes diimplementasikan untuk mengklasifikasikan apakah sebuah record pada data testing merupakan serangan Shellcode atau non-Shellcode. Pengklasifikasi KNN, Decision Tree, dan Naïve Bayes mencapai tingkat akurasi masing-masing sebesar 96,82%, 97,08%, dan 63,43%. Hasil dari penelitian ini diharapkan dapat memberikan wawasan mengenai penggunaan machine learning dalam mendeteksi atau mengklasifikasikan malware atau jenis serangan siber lainnya.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Klasifikasi Biner, Cyber Security, Shellcode, Machine Learning, Supervised Machine Learning, Hyperparameter Tuning
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television > TK5105.5.S72 Computer networks
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware
Divisions: 09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User: Jaka Naufal Semendawai
Date Deposited: 18 Nov 2024 06:30
Last Modified: 18 Nov 2024 06:30
URI: http://repository.unsri.ac.id/id/eprint/159518

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